main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pybtex.database import parse_file\n",
    "\n",
    "bib_data = parse_file('assets/literature.bib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "    ('title', 'The Processes of Creative Thinking'), \n",
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       "    ('title', '{I.—COMPUTING MACHINERY AND INTELLIGENCE}'), \n",
       "    ('journal', 'Mind'), \n",
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       "    ('doi', '10.1093/mind/LIX.236.433'), \n",
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       "    ('title', '{COINS}: Dynamically Generating {CO}ntextualized Inference Rules for Narrative Story Completion'), \n",
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       "    ('booktitle', 'Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)'), \n",
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       "    ('url', 'https://aclanthology.org/2024.acl-long.706'), \n",
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       "    ('title', 'Creating a Story-Telling Universe'), \n",
       "    ('booktitle', 'International Joint Conference on Artificial Intelligence'), \n",
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       "    ('title', 'MEXICA: A computer model of a cognitive account of creative writing'), \n",
       "    ('journal', 'Journal of Experimental \\\\& Theoretical Artificial Intelligence'), \n",
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       "    ('title', 'The Creative Process: A Computer Model of Storytelling and Creativity'), \n",
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       "    ('title', 'Attention is All you Need'), \n",
       "    ('booktitle', 'Neural Information Processing Systems'), \n",
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       "    ('archivePrefix', 'arXiv'), \n",
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       "    ('title', 'A Survey of Large Language Models'), \n",
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       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Zhao, Wayne Xin'), Person('Zhou, Kun'), Person('Li, Junyi'), Person('Tang, Tianyi'), Person('Wang, Xiaolei'), Person('Hou, Yupeng'), Person('Min, Yingqian'), Person('Zhang, Beichen'), Person('Zhang, Junjie'), Person('Dong, Zican'), Person('Du, Yifan'), Person('Yang, Chen'), Person('Chen, Yushuo'), Person('Chen, Zhipeng'), Person('Jiang, Jinhao'), Person('Ren, Ruiyang'), Person('Li, Yifan'), Person('Tang, Xinyu'), Person('Liu, Zikang'), Person('Liu, Peiyu'), Person('Nie, Jian-Yun'), Person('Wen, Ji-Rong')])]))), ('bubeck2023sparks', Entry('misc',\n",
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       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL')],\n",
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       "    ('title', 'The Nature of Human Intelligence'), \n",
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       "    ('title', 'Re3: Generating Longer Stories With Recursive Reprompting and Revision'), \n",
       "    ('booktitle', 'Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing'), \n",
       "    ('month', 'December'), \n",
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       "    ('address', 'Abu Dhabi, United Arab Emirates'), \n",
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       "    ('url', 'https://aclanthology.org/2022.emnlp-main.296'), \n",
       "    ('doi', '10.18653/v1/2022.emnlp-main.296'), \n",
       "    ('pages', '4393--4479'), \n",
       "    ('abstract', \"We consider the problem of automatically generating longer stories of over two thousand words. Compared to prior work on shorter stories, long-range plot coherence and relevance are more central challenges here. We propose the Recursive Reprompting and Revision framework (Re3) to address these challenges by (a) prompting a general-purpose language model to construct a structured overarching plan, and (b) generating story passages by repeatedly injecting contextual information from both the plan and current story state into a language model prompt. We then revise by (c) reranking different continuations for plot coherence and premise relevance, and finally (d) editing the best continuation for factual consistency. Compared to similar-length stories generated directly from the same base model, human evaluators judged substantially more of Re3{'}s stories as having a coherent overarching plot (by 14{\\\\%} absolute increase), and relevant to the given initial premise (by 20{\\\\%}).\")],\n",
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       "    ('booktitle', 'Conference on Empirical Methods in Natural Language Processing'), \n",
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       "    ('title', 'Help me write a Poem - Instruction Tuning as a Vehicle for Collaborative Poetry Writing'), \n",
       "    ('journal', 'ArXiv'), \n",
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       "    ('volume', 'abs/2210.13669'), \n",
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       "    ('title', 'Jukebox: A Generative Model for Music'), \n",
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       "    ('title', 'How Much Do Language Models Copy From Their Training Data? Evaluating Linguistic Novelty in Text Generation Using RAVEN'), \n",
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       "    ('doi', 'https://doi.org/10.1111/j.1747-9991.2010.00351.x'), \n",
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       "    ('eprint', 'https://compass.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1747-9991.2010.00351.x'), \n",
       "    ('abstract', 'Abstract This paper surveys some of the central issues in the philosophy of creativity and argues that an adequate treatment of them requires attention to the rich psychological literature on creativity. It also shows that the range of interesting philosophical questions to be raised about creativity is much wider than concerns its role in art. Issues covered include the definition of ‘creativity’; the relation of creativity to imagination; whether the creative process is rational; whether it is teleological; the relation of creativity to knowledge; whether creativity can be explained; computational and Darwinian theories of creativity; whether creativity is a virtue; the relation of creativity to tradition; the aesthetic value of creativity; and whether creative activity is different in science and art.'), \n",
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       "    ('abstract', '{I argue that creativity is compatible with determinism and therefore with naturalistic explanation. I explore different kinds of novelty, corresponding with four distinct concepts of creativity – anthropological, historical, psychological and metaphysical. Psychological creativity incorporates originality and spontaneity. Taken together, these point to the independence of the creative mind from social learning, experience and previously acquired knowledge. This independence is nevertheless compatible with determinism. Creativity is opposed to specific causal factors, but it does not exclude causal determination as such. So creativity can be naturalized.}'), \n",
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       "    ('eprint', '2306.11167'), \n",
       "    ('archivePrefix', 'arXiv')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Naeini, Saeid'), Person('Saqur, Raeid'), Person('Saeidi, Mozhgan'), Person('Giorgi, John'), Person('Taati, Babak')])]))), ('Franceschelli2024CreativeBS', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Creative Beam Search: LLM-as-a-Judge For Improving Response Generation'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2024'), \n",
       "    ('volume', 'abs/2405.00099'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:269484318')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Franceschelli, Giorgio'), Person('Musolesi, Mirco')])]))), ('elgammal2017can', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'CAN: Creative Adversarial Networks, Generating \"Art\" by Learning About Styles and Deviating from Style Norms'), \n",
       "    ('year', '2017'), \n",
       "    ('eprint', '1706.07068'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.AI')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Elgammal, Ahmed'), Person('Liu, Bingchen'), Person('Elhoseiny, Mohamed'), Person('Mazzone, Marian')])]))), ('aleinikov2000creating', Entry('book',\n",
       "  fields=[\n",
       "    ('title', 'Creating Creativity: 101 Definitions (what Webster Never Told You)'), \n",
       "    ('isbn', '9780970311009'), \n",
       "    ('url', 'https://books.google.ch/books?id=M2QpAAAACAAJ'), \n",
       "    ('year', '2000'), \n",
       "    ('publisher', 'Alden B. Dow Creativity Center Press')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Aleinikov, A.G.'), Person('Kackmeister, S.'), Person('Koenig, R.')])]))), ('runco2012std', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'The Standard Definition of Creativity'), \n",
       "    ('journal', 'Creativity Research Journal'), \n",
       "    ('volume', '24'), \n",
       "    ('number', '1'), \n",
       "    ('pages', '92-96'), \n",
       "    ('year', '2012'), \n",
       "    ('publisher', 'Routledge'), \n",
       "    ('doi', '10.1080/10400419.2012.650092'), \n",
       "    ('URL', 'https://doi.org/10.1080/10400419.2012.650092'), \n",
       "    ('eprint', 'https://doi.org/10.1080/10400419.2012.650092')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Runco, Mark A.'), Person('Jaeger, Garrett J.')])]))), ('barron1955disposition', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'The disposition toward originality.'), \n",
       "    ('journal', 'The Journal of Abnormal and Social Psychology'), \n",
       "    ('volume', '51'), \n",
       "    ('number', '3'), \n",
       "    ('pages', '478'), \n",
       "    ('year', '1955'), \n",
       "    ('publisher', 'American Psychological Association')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Barron, Frank')])]))), ('stein1953creativity', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Creativity and culture'), \n",
       "    ('journal', 'The journal of psychology'), \n",
       "    ('volume', '36'), \n",
       "    ('number', '2'), \n",
       "    ('pages', '311--322'), \n",
       "    ('year', '1953'), \n",
       "    ('publisher', 'Taylor \\\\& Francis')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Stein, Morris I')])]))), ('rudolph2001-cc', Entry('article',\n",
       "  fields=[\n",
       "    ('year', '2001'), \n",
       "    ('month', '01'), \n",
       "    ('pages', ''), \n",
       "    ('title', 'What it Means to be Creative'), \n",
       "    ('volume', '41'), \n",
       "    ('journal', 'British Journal of Aesthetics'), \n",
       "    ('doi', '10.1093/bjaesthetics/41.1.24')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Arnheim, Rudolf')])]))), ('weisberg2015value', Entry('article',\n",
       "  fields=[\n",
       "    ('year', '2015'), \n",
       "    ('month', '06'), \n",
       "    ('pages', '111-124'), \n",
       "    ('title', 'On the Usefulness of “Value” in the Definition of Creativity'), \n",
       "    ('volume', '27'), \n",
       "    ('journal', 'Creativity Research Journal'), \n",
       "    ('doi', '10.1080/10400419.2015.1030320')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Weisberg, Robert')])]))), ('anderson2024', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Homogenization Effects of Large Language Models on Human Creative Ideation'), \n",
       "    ('year', '2024'), \n",
       "    ('isbn', '9798400704857'), \n",
       "    ('publisher', 'Association for Computing Machinery'), \n",
       "    ('address', 'New York, NY, USA'), \n",
       "    ('url', 'https:\n",
       "    //doi.org/10.1145/3635636.3656204'), ('doi', '10.1145/3635636.3656204'), \n",
       "    ('booktitle', 'Proceedings of the 16th Conference on Creativity and Cognition'), \n",
       "    ('pages', '413–425'), \n",
       "    ('numpages', '13'), \n",
       "    ('keywords', 'creativity support tools, divergent ideation, large language models, user study'), \n",
       "    ('location', 'Chicago, IL, USA')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Anderson, Barrett R'), Person('Shah, Jash Hemant'), Person('Kreminski, Max')])]))), ('std-def-creativity', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'The Standard Definition of Creativity'), \n",
       "    ('journal', 'Creativity Research Journal'), \n",
       "    ('volume', '24'), \n",
       "    ('number', '1'), \n",
       "    ('pages', '92-96'), \n",
       "    ('year', '2012'), \n",
       "    ('publisher', 'Routledge'), \n",
       "    ('doi', '10.1080/10400419.2012.650092'), \n",
       "    ('URL', 'https://doi.org/10.1080/10400419.2012.650092'), \n",
       "    ('eprint', 'https://doi.org/10.1080/10400419.2012.650092')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Runco, Mark A.'), Person('Jaeger, Garrett J.')])]))), ('kant1790critique', Entry('book',\n",
       "  fields=[\n",
       "    ('title', 'Critique of Judgment'), \n",
       "    ('isbn', '9781602065420'), \n",
       "    ('series', 'Cosimo Classics'), \n",
       "    ('url', 'https://books.google.ch/books?id=yBFsrJqNtygC'), \n",
       "    ('year', '1790'), \n",
       "    ('publisher', 'Cosimo')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Kant, I.')])]))), ('stokes2011', Entry('article',\n",
       "  fields=[\n",
       "    ('ISSN', '00261068, 14679973'), \n",
       "    ('URL', 'http://www.jstor.org/stable/24439973'), \n",
       "    ('abstract', 'Creativity has received, and continues to receive, comparatively little analysis in philosophy and the brain and behavioural sciences. This is in spite of the importance of creative thought and action, and the many and varied resources of theories of mind. Here an alternative approach to analyzing creativity is suggested: start from the bottom up with minimally creative thought. Minimally creative thought depends non-accidentally upon agency, is novel relative to the acting agent, and could not have been tokened before the time it is in fact tokened, relative to the agent in question. Thoughts that meet these three conditions—agency, psychological novelty, and modal—are what may be called cognitive breakthroughs. Even if such breakthroughs are not necessary to or definitive of richer creativity, they are indeed central to much of creativity. The minimal analysis provides a more workable explanandum for theories of creativity of varied motivation and method.'), \n",
       "    ('journal', 'Metaphilosophy'), \n",
       "    ('number', '5'), \n",
       "    ('pages', '658--681'), \n",
       "    ('publisher', 'Wiley'), \n",
       "    ('title', 'Minimally Creative Thought'), \n",
       "    ('urldate', '2024-01-24'), \n",
       "    ('volume', '42'), \n",
       "    ('year', '2011')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Stokes, Dustin')])]))), ('novitz1999', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Creativity and Constraint'), \n",
       "    ('journal', 'Australasian Journal of Philosophy'), \n",
       "    ('volume', '77'), \n",
       "    ('number', '1'), \n",
       "    ('pages', '67-82'), \n",
       "    ('year', '1999'), \n",
       "    ('publisher', 'Routledge'), \n",
       "    ('doi', '10.1080/00048409912348811'), \n",
       "    ('URL', 'https://doi.org/10.1080/00048409912348811'), \n",
       "    ('eprint', 'https://doi.org/10.1080/00048409912348811')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Novitz, David')])]))), ('sep-creativity', Entry('incollection',\n",
       "  fields=[\n",
       "    ('title', '{Creativity}'), \n",
       "    ('booktitle', 'The {Stanford} Encyclopedia of Philosophy'), \n",
       "    ('howpublished', '\\\\url{https://plato.stanford.edu/archives/spr2023/entries/creativity/}'), \n",
       "    ('year', '2023'), \n",
       "    ('edition', '{S}pring 2023'), \n",
       "    ('publisher', 'Metaphysics Research Lab, Stanford University')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Paul, Elliot Samuel'), Person('Stokes, Dustin')]), ('editor', [Person('Zalta, Edward N.'), Person('Nodelman, Uri')])]))), ('Livingston2018-LIVEQ', Entry('incollection',\n",
       "  fields=[\n",
       "    ('bo\n",
       "    oktitle', 'Routledge Handbook on Creativity and Philosophy'), \n",
       "    ('pages', '108--123'), \n",
       "    ('publisher', 'Routledge'), ('title', '\\'Explicating \"Creativity\"'), \n",
       "    ('year', '2018')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Livingston, Paisley')]), ('editor', [Person('Gaut, Berys'), Person('Kieran, Matthew')])]))), ('wallas1926art', Entry('book',\n",
       "  fields=[\n",
       "    ('title', 'The Art of Thought'), \n",
       "    ('isbn', '9781910146057'), \n",
       "    ('url', 'https://books.google.ch/books?id=JR6boAEACAAJ'), \n",
       "    ('year', '1926'), \n",
       "    ('publisher', 'Solis Press')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Wallas, G.')])]))), ('rhodes1961fourp', Entry('article',\n",
       "  fields=[\n",
       "    ('ISSN', '00317217'), \n",
       "    ('URL', 'http://www.jstor.org/stable/20342603'), \n",
       "    ('journal', 'The Phi Delta Kappan'), \n",
       "    ('number', '7'), \n",
       "    ('pages', '305--310'), \n",
       "    ('publisher', 'Phi Delta Kappa International'), \n",
       "    ('title', 'An Analysis of Creativity'), \n",
       "    ('urldate', '2024-01-24'), \n",
       "    ('volume', '42'), \n",
       "    ('year', '1961')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Rhodes, Mel')])]))), ('Mednick1962TheAB', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'The associative basis of the creative process.'), \n",
       "    ('journal', 'Psychological review'), \n",
       "    ('year', '1962'), \n",
       "    ('volume', '69'), \n",
       "    ('pages', '220-32'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:6702759')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Mednick, Sarnoff A.')])]))), ('Glveanu2013RewritingTL', Entry('article',\n",
       "  fields=[\n",
       "    ('title', \"Rewriting the Language of Creativity: The Five A's Framework\"), \n",
       "    ('journal', 'Review of General Psychology'), \n",
       "    ('year', '2013'), \n",
       "    ('volume', '17'), \n",
       "    ('pages', '69 - 81'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:143404705')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Glăveanu, Vlad Petre')])]))), ('amabile1996creativity', Entry('book',\n",
       "  fields=[\n",
       "    ('title', 'Creativity In Context: Update To The Social Psychology Of Creativity'), \n",
       "    ('isbn', '9780813345499'), \n",
       "    ('url', 'https://books.google.ch/books?id=pZI4DgAAQBAJ'), \n",
       "    ('year', '1996'), \n",
       "    ('publisher', 'Avalon Publishing')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Amabile, T.M.')])]))), ('sternberg1991invest', Entry('article',\n",
       "  fields=[\n",
       "    ('ISSN', '0018716X, 14230054'), \n",
       "    ('URL', 'http://www.jstor.org/stable/26767348'), \n",
       "    ('abstract', 'This article presents an investment theory of creativity. The theory comprises 6 resources for creativity – intellectual processes, knowledge, intellectual style, personality, motivation, and environmental context. Creative performance results from a confluence of these elements. Main features of each resource are explained and the manner in which the 6 resources combine is discussed. Then a preliminary empirical study that tests aspects of the investment theory is briefly presented. Next, the development of creativity in terms of the 6 resources is described. Finally, potential criticisms of the investment theory are addressed. The goal of the theory is to understand in a cohesive way the foundations of creativity. To the extent that true creativity seems rare, it may be because many people are not willing to invest in it and because so many resources must converge in order to generate it.'), \n",
       "    ('journal', 'Human Development'), \n",
       "    ('number', '1'), \n",
       "    ('pages', '1--31'), \n",
       "    ('publisher', 'S. Karger AG'), \n",
       "    ('title', 'An Investment Theory of Creativity and Its Development'), \n",
       "    ('urldate', '2024-01-25'), \n",
       "    ('volume', '34'), \n",
       "    ('year', '1991')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Sternberg, Robert J.'), Person('Lubart, Todd I.')])]))), ('sternberg2016triang', Entry('article',\n",
       "  fields=[\n",
       "    ('year', '2016'), \n",
       "    ('month', '12'), \n",
       "    ('pages', ''), \n",
       "    ('title', 'A Triangular Theory of Creativity'), \n",
       "    ('volume', '12'), \n",
       "    ('journal', 'Psychology of Aesthetics, Creativity, and the Arts'), \n",
       "    ('doi', '10.1037/aca0000095')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Sternberg, Robert')])]))), ('beghetto2022theories', Entry('inbook',\n",
       "  fields=[\n",
       "    ('year', '2022'), \n",
       "    ('month', '02'), \n",
       "    ('pages', '23-36'), \n",
       "    ('title', 'Theories of Creativity'), \n",
       "    ('isbn', '9781003233923'), \n",
       "    ('doi', '10.4324/9781003233923-3')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Beghetto, Ronald'), Person('Kaufman, James')])]))), ('schmidhuber2010fun', Entry('article',\n",
       "  fields=[\n",
       "    ('journal', 'IEEE Transactions on Autonomous Mental Development'), \n",
       "    ('title', 'Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010)'), \n",
       "    ('year', '2010'), \n",
       "    ('volume', '2'), \n",
       "    ('number', '3'), \n",
       "    ('pages', '230-247'), \n",
       "    ('keywords', 'Art;Psychology;Pediatrics;Predictive models;Data compression;Computational intelligence;Intelligent robots;Feedback;Eyes;Fingers;Active learning;aesthetics theory;art;attention;developmental psychology;formal theory of creativity;fun;humor;limited computational resources;music;novel patterns;novelty;science;surprise;typology of intrinsic motivation'), \n",
       "    ('doi', '10.1109/TAMD.2010.2056368')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Schmidhuber, Jürgen')])]))), ('Rowe1993CreativityAS', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Creativity: a survey of AI approaches'), \n",
       "    ('journal', 'Artificial Intelligence Review'), \n",
       "    ('year', '1993'), \n",
       "    ('volume', '7'), \n",
       "    ('pages', '43-70'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:13433112')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Rowe, Jon'), Person('Partridge, Derek')])]))), ('Franceschelli2021CreativityAM', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Creativity and Machine Learning: A Survey'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2021'), \n",
       "    ('volume', 'abs/2104.02726'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:233168627')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Franceschelli, Giorgio'), Person('Musolesi, Mirco')])]))), ('chomsky1965aspects', Entry('book',\n",
       "  fields=[\n",
       "    ('ISBN', '9780262527408'), \n",
       "    ('URL', 'http://www.jstor.org/stable/j.ctt17kk81z'), \n",
       "    ('abstract', 'Noam Chomsky\\'sAspects of the Theory of Syntax, published in 1965, was a landmark work in generative grammar that introduced certain technical innovations still drawn upon in contemporary work. The fiftieth anniversar\n",
       "    y edition of this influential book includes a new preface by the author that identifies proposals that seem to be of lasting significance, reviews changes and improvements in the formulation and implementation of basic ideas, and addresses some of the controversies that arose over the general framework.Beginning in the mid-fifties and emanating largely from MIT, linguists developed an approach to linguistic theory and to the study of the structure of particular languages that diverged in many respects from conventional modern linguistics. Although the new approach was connected to the traditional study of languages, it differed enough in its specific conclusions about the structure of language to warrant a name, \"generative grammar.\" Various deficiencies were discovered in the first attempts to formulate a theory of transformational generative grammar and in the descriptive analysis of particular languages that motivated these formulations. At the same time, it became apparent that these formulations can be extended and deepened. In this book, Chomsky reviews these developments and proposes a reformulation of the theory of transformational generative grammar that takes them into account. The emphasis in this study is syntax; semantic and phonological aspects of the language structure are discussed only insofar as they bear on syntactic theory.'), ('edition', '50'), \n",
       "    ('publisher', 'The MIT Press'), \n",
       "    ('title', 'Aspects of the Theory of Syntax'), \n",
       "    ('urldate', '2024-01-25'), \n",
       "    ('year', '1965')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Chomsky, Noam')])]))), ('bergs2019ling', Entry('article',\n",
       "  fields=[\n",
       "    ('doi', 'doi:10.2478/gth-2019-0017'), \n",
       "    ('url', 'https://doi.org/10.2478/gth-2019-0017'), \n",
       "    ('title', 'What, If Anything, Is Linguistic Creativity?'), \n",
       "    ('journal', 'Gestalt Theory'), \n",
       "    ('number', '2'), \n",
       "    ('volume', '41'), \n",
       "    ('year', '2019'), \n",
       "    ('pages', '173--183')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Bergs, Alexander')])]))), ('sampson2017ling', Entry('book',\n",
       "  fields=[\n",
       "    ('ISBN', '9781781796061'), \n",
       "    ('URL', 'https://www.equinoxpub.com/home/linguistics-delusion/'), \n",
       "    ('publisher', 'Equinox Publishing'), \n",
       "    ('title', 'The Linguistics Delusion'), \n",
       "    ('urldate', '2017-09-14'), \n",
       "    ('year', '2017')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Sampson, Geoffrey')])]))), ('zawada2006ling', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Linguistic creativity from a cognitive perspective'), \n",
       "    ('journal', 'Southern African Linguistics and Applied Language Studies'), \n",
       "    ('volume', '24'), \n",
       "    ('number', '2'), \n",
       "    ('pages', '235-254'), \n",
       "    ('year', '2006'), \n",
       "    ('publisher', 'Routledge'), \n",
       "    ('doi', '10.2989/16073610609486419'), \n",
       "    ('URL', 'https://doi.org/10.2989/16073610609486419'), \n",
       "    ('eprint', 'https://doi.org/10.2989/16073610609486419')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Zawada, Britta')])]))), ('lebowitz1984creating', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Creating characters in a story-telling universe'), \n",
       "    ('journal', 'Poetics'), \n",
       "    ('volume', '13'), \n",
       "    ('number', '3'), \n",
       "    ('pages', '171-194'), \n",
       "    ('year', '1984'), \n",
       "    ('issn', '0304-422X'), \n",
       "    ('doi', 'https://doi.org/10.1016/0304-422X(84)90001-9'), \n",
       "    ('url', 'https://www.sciencedirect.com/science/article/pii/0304422X84900019'), \n",
       "    ('abstract', 'Extended story generation, i.e., the creation of continuing serials, presents difficult and interesting problems for Artificial Intelligence. We present here the first phase of the development of a program, UNIVERSE, that will ultimately tell extended stories. In particular, after describing our overall model of story telling, we present a method for creating universes of characters appropriate for extended story generation. This method concentrates on the need to keep story-telling universes consistent and coherent. We also describe the information that must be maintained for characters and interpersonal relationships, and the use of stereotypical information about people to help motivate trait values. The use of historical events for motivation is also described. Finally, we present an example of a character generated by UNIVERSE.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Lebowitz, Michael')])]))), ('yao2019plan', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Plan-And-Write: Towards Better Automatic Storytelling'), \n",
       "    ('booktitle', 'The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)'), \n",
       "    ('year', '2019')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Yao, Lili'), Person('Peng, Nanyun'), Person('Ralph, Weischedel'), Person('Knight, Kevin'), Person('Zhao, Dongyan'), Person('Yan, Rui')])]))), ('goldfarb2020content', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Content Planning for Neural Story Generation with Aristotelian Rescoring'), \n",
       "    ('booktitle', 'the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)'), \n",
       "    ('pages', '4319--4338'), \n",
       "    ('slideslive_id', '38939240'), \n",
       "    ('year', '2020')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Goldfarb-Tarrant, Seraphina'), Person('Chakrabarty, Tuhin'), Person('Weischedel, Ralph'), Person('Peng, Nanyun')])]))), ('chakrabarty-etal-2023-creative', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Creative Natural Language Generation'), \n",
       "    ('booktitle', 'Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts'), \n",
       "    ('month', 'December'), \n",
       "    ('year', '2023'), \n",
       "    ('address', 'Singapore'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2023.emnlp-tutorial.6'), \n",
       "    ('doi', '10.18653/v1/2023.emnlp-tutorial.6'), \n",
       "    ('pages', '34--40'), \n",
       "    ('abstract', 'Large language models such as GPT-3, GPT4, Claude etc., have advanced the state of the art in several natural language generation tasks such as text summarization and machine translation. However when it comes to open-ended tasks with a focus on creativity such as generating stories, poetry, or various forms of figurative language, these state-of-the-art language models are often found to be inadequate. This tutorial aims to bring awareness of the important and emerging research area of open-domain creative generation, with a focus on language generation while also touching on multi-modal generation (e.g., image captioning, visual metaphors). It targets natural language processing (NLP) and artificial intelligence (AI) researchers as well as creative writing practitioners who are interested in building systems that are capable of emulating as well as augmenting human creativity. In particular, we will review recent studies on creative language generation both at the sentence level as well as longer forms of text. We will provide the audiences with a holistic view of 1) the importance and challenges of building creative language generation systems; 2) how we incorporate content planning, domain knowledge and creativity specific heuristics for different forms of creative language generation such as story, poetry, humor, metaphors etc 3) how can we build better evaluation methods for creative text generation? In particular, how could the recent advancement of AI shape the future workforce for creativity? We will conclude the tutorial by outlining future research directions in this area.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Chakrabarty, Tuhin'), Person('Padmakumar, Vishakh'), Person('He, He'), Person('Peng, Nanyun')]), ('editor', [Person('Zhang, Qi'), Person('Sajjad, Hassan')])]))), ('chakrabarty2023art', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Art or Artifice? Large Language Models and the False Promise of Creativity'), \n",
       "    ('year', '2023'), \n",
       "    ('eprint', '2309.14556'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Chakrabarty, Tuhin'), Person('Laban, Philippe'), Person('Agarwal, Divyansh'), Person('Muresan, Smaranda'), Person('Wu, Chien-Sheng')])]))), ('manurung2000flexible', Entry('article',\n",
       "  fields=[\n",
       "    ('year', '2000'), \n",
       "    ('month', '07'), \n",
       "    ('pages', ''), \n",
       "    ('title', 'A Flexible Integrated Architecture For Generating Poetic Texts')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Manurung, Ruli'), Person('Ritchie, Graeme'), Person('Thompson, Henry')])]))), ('Ren2017NeuralJG', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Neural Joke Generation'), \n",
       "    ('year', '2017'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:19647037')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Ren, He'), Person('Yang, Quan')])]))), ('veale2016metaphor', Entry('book',\n",
       "  fields=[\n",
       "    ('title', 'Metaphor: A Computational Perspective'), \n",
       "    ('isbn', '9781627058513'), \n",
       "    ('series', 'Synthesis Lectures on Human Language Technologies'), \n",
       "    ('url', 'https://books.google.ch/books?id=_ripCwAAQBAJ'), \n",
       "    ('year', '2016'), \n",
       "    ('publisher', 'Morgan \\\\& Claypool Publishers')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Veale, T.'), Person('Shutova, E.'), Person('Klebanov, B.')])]))), ('Paul1970-PAUFL', Entry('article',\n",
       "  fields=[\n",
       "    ('journal', 'Philosophy and Rhetoric'), \n",
       "    ('number', '4'), \n",
       "    ('pages', '225--248'), \n",
       "    ('publisher', 'Pennsylvania State University Press'), \n",
       "    ('title', 'Figurative Language'), \n",
       "    ('volume', '3'), \n",
       "    ('year', '1970')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Paul, Anthony M.')])]))), ('dhar2019learning', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Learning to Predict Novel Noun-Noun Compounds'), \n",
       "    ('year', '2019'), \n",
       "    ('eprint', '1906.03634'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Dhar, Prajit'), Person('van der Plas, Lonneke')])]))), ('lencione2022nameling', Entry('article',\n",
       "  fields=[\n",
       "    ('year', '2022'), \n",
       "    ('month', '06'), \n",
       "    ('title', 'Nameling: Creative Neologism Generation with Transfer Learning'), \n",
       "    ('journal', 'International Conference on Computational Creativity')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Lencione, Gabriel R'), Person('Nogueira, Rodrigo F'), Person('Pasqualini, Paula Y')])]))), ('roberts1994people', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Why do people use figurative language?'), \n",
       "    ('journal', 'Psychological science'), \n",
       "    ('volume', '5'), \n",
       "    ('number', '3'), \n",
       "    ('pages', '159--163'), \n",
       "    ('year', '1994'), \n",
       "    ('publisher', 'SAGE Publications Sage CA: Los Angeles, CA')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Roberts, Richard M'), Person('Kreuz, Roger J')])]))), ('Suzuki9468', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Dialogues: The Science and Power of Storytelling'), \n",
       "    ('volume', '38'), \n",
       "    ('number', '44'), \n",
       "    ('pages', '9468--9470'), \n",
       "    ('year', '2018'), \n",
       "    ('doi', '10.1523/JNEUROSCI.1942-18.2018'), \n",
       "    ('publisher', 'Society for Neuroscience'), \n",
       "    ('abstract', 'Skillful storytelling helps listeners understand the essence of complex concepts and ideas in meaningful and often personal ways. For this reason, storytelling is being embraced by scientists who not only want to connect more authentically with their audiences, but also want to understand how the brain processes this powerful form of communication. Here we present part of a conversation between a group of scientists actively engaged with the practice and/or the science of storytelling. We highlight the brain networks involved in the telling and hearing of stories and show how storytelling is being used well beyond the realm of public communication to add a deeper dimension to communication with our students and colleagues, as well as helping to make our profession more inclusive.'), \n",
       "    ('issn', '0270-6474'), \n",
       "    ('URL', 'https://www.jneurosci.org/content/38/44/9468'), \n",
       "    ('eprint', 'https://www.jneurosci.org/content/38/44/9468.full.pdf'), \n",
       "    ('journal', 'Journal of Neuroscience')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Suzuki, Wendy A.'), Person(\"Feli{\\\\'u}-M{\\\\'o}jer, M{\\\\'o}nica I.\"), Person('Hasson, Uri'), Person('Yehuda, Rachel'), Person('Zarate, Jean Mary')])]))), ('weissweiler-etal-2023-counting', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', \"Counting the Bugs in {C}hat{GPT}{'}s Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model\"), \n",
       "    ('booktitle', 'Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing'), \n",
       "    ('month', 'December'), \n",
       "    ('year', '2023'), \n",
       "    ('address', 'Singapore'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2023.emnlp-main.401'), \n",
       "    ('doi', '10.18653/v1/2023.emnlp-main.401'), \n",
       "    ('pages', '6508--6524'), \n",
       "    ('abstract', \"Large language models (LLMs) have recently reached an impressive level of linguistic capability, prompting comparisons with human language skills. However, there have been relatively few systematic inquiries into the linguistic capabilities of the latest generation of LLMs, and those studies that do exist (i) ignore the remarkable ability of humans to generalize, (ii) focus only on English, and (iii) investigate syntax or semantics and overlook other capabilities that lie at the heart of human language, like morphology. Here, we close these gaps by conducting the first rigorous analysis of the morphological capabilities of ChatGPT in four typologically varied languages (specifically, English, German, Tamil, and Turkish). We apply a version of Berko{'}s (1958) wug test to ChatGPT, using novel, uncontaminated datasets for the four examined languages. We find that ChatGPT massively underperforms purpose-built systems, particularly in English. Overall, our results{---}through the lens of morphology{---}cast a new light on the linguistic capabilities of ChatGPT, suggesting that claims of human-like language skills are premature and misleading.\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Weissweiler, Leonie'), Person('Hofmann, Valentin'), Person('Kantharuban, Anjali'), Person('Cai, Anna'), Person('Dutt, Ritam'), Person('Hengle, Amey'), Person('Kabra, Anubha'), Person('Kulkarni, Atharva'), Person('Vijayakumar, Abhishek'), Person('Yu, Haofei'), Person('Schuetze, Hinrich'), Person('Oflazer, Kemal'), Person('Mortensen, David')]), ('editor', [Person('Bouamor, Houda'), Person('Pino, Juan'), Person('Bali, Kalika')])]))), ('duncker1948problem', Entry('book',\n",
       "  fields=[\n",
       "    ('title', 'On Problem-solving'), \n",
       "    ('series', 'Psychological monographs'), \n",
       "    ('url', 'https://books.google.ch/books?id=g888tAEACAAJ'), \n",
       "    ('year', '1948'), \n",
       "    ('publisher', 'American Psychological Ass.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Duncker, K.'), Person('Lees, L.S.')])]))), ('davidson_gureckis_lake_2022', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Creativity, Compositionality, and Common Sense in Human Goal Generation'), \n",
       "    ('url', 'osf.io/preprints/psyarxiv/byzs5'), \n",
       "    ('DOI', '10.31234/osf.io/byzs5'), \n",
       "    ('publisher', 'PsyArXiv'), \n",
       "    ('year', '2022'), \n",
       "    ('month', 'Mar')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Davidson, Guy'), Person('Gureckis, Todd M'), Person('Lake, Brenden M')])]))), ('tian2023macgyver', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'MacGyver: Are Large Language Models Creative Problem Solvers?'), \n",
       "    ('year', '2023'), \n",
       "    ('eprint', '2311.09682'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Tian, Yufei'), Person('Ravichander, Abhilasha'), Person('Qin, Lianhui'), Person('Bras, Ronan Le'), Person('Marjieh, Raja'), Person('Peng, Nanyun'), Person('Choi, Yejin'), Person('Griffiths, Thomas L.'), Person('Brahman, Faeze')])]))), ('pease2002evaluate', Entry('article',\n",
       "  fields=[\n",
       "    ('year', '2002'), \n",
       "    ('month', '10'), \n",
       "    ('pages', ''), \n",
       "    ('title', 'Evaluating Machine Creativity')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Pease, Alison'), Person('Winterstein, Daniel'), Person('Colton, Simon')])]))), ('Colton2011ComputationalCT', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Computational Creativity Theory: The FACE and IDEA Descriptive Models'), \n",
       "    ('booktitle', 'International Conference on Innovative Computing and Cloud Computing'), \n",
       "    ('year', '2011'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:522774')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Colton, Simon'), Person('Charnley, John William'), Person('Pease, Alison')])]))), ('Ritchie2001AssessingC', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Assessing Creativity'), \n",
       "    ('year', '2001'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:925777')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Ritchie, Graeme D.')])]))), ('ritchie2007some', Entry('article',\n",
       "  fields=[\n",
       "    ('year', '2007'), \n",
       "    ('month', '07'), \n",
       "    ('pages', '67-99'), \n",
       "    ('title', 'Some Empirical Criteria for Attributing Creativity to a Computer Program'), \n",
       "    ('volume', '17'), \n",
       "    ('journal', 'Minds and Machines'), \n",
       "    ('doi', '10.1007/s11023-007-9066-2')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Ritchie, Graeme')])]))), ('bringsjord2000creat', Entry('article',\n",
       "  fields=[\n",
       "    ('year', '2000'), \n",
       "    ('month', '06'), \n",
       "    ('pages', ''), \n",
       "    ('title', 'Creativity, the Turing Test, and the (Better) Lovelace Test'), \n",
       "    ('volume', '11'), \n",
       "    ('isbn', '978-1-4020-1205-1'), \n",
       "    ('journal', 'Minds and Machines'), \n",
       "    ('doi', '10.1023/A:1011206622741')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Bringsjord, Selmer'), Person('Bello, Paul'), Person('Ferrucci, David')])]))), ('Jordanous2012ASP', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'A Standardised Procedure for Evaluating Creative Systems: Computational Creativity Evaluation Based on What it is to be Creative'), \n",
       "    ('journal', 'Cognitive Computation'), \n",
       "    ('year', '2012'), \n",
       "    ('volume', '4'), \n",
       "    ('pages', '246 - 279'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:311189')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Jordanous, Anna')])]))), ('webb2017once', Entry('article',\n",
       "  fields=[\n",
       "    ('year', '2017'), \n",
       "    ('month', '10'), \n",
       "    ('pages', ''), \n",
       "    ('title', 'Once more with feeling: Normative data for the aha experience in insight and noninsight problems'), \n",
       "    ('volume', '50'), \n",
       "    ('journal', 'Behavior Research Methods'), \n",
       "    ('doi', '10.3758/s13428-017-0972-9')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Webb, Margaret'), Person('Little, Daniel'), Person('Cropper, Simon')])]))), ('torrance1974torrance', Entry('book',\n",
       "  fields=[\n",
       "    ('title', 'Torrance Tests of Creative Thinking: Verbal Tests, Forms A and B, Figural Tests, Forms A and B'), \n",
       "    ('isbn', '9780663292523'), \n",
       "    ('series', 'Norms-technical manual'), \n",
       "    ('url', 'https://books.google.ch/books?id=_4dUYAAACAAJ'), \n",
       "    ('year', '1974'), \n",
       "    ('publisher', 'Xerox')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Torrance, E.P.')])]))), ('stevenson2022putting', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', \"Putting GPT-3's Creativity to the (Alternative Uses) Test\"), \n",
       "    ('year', '2022'), \n",
       "    ('eprint', '2206.08932'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.AI')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Stevenson, Claire'), Person('Smal, Iris'), Person('Baas, Matthijs'), Person('Grasman, Raoul'), Person('van der Maas, Han')])]))), ('openai2023gpt4', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'GPT-4 Technical Report'), \n",
       "    ('year', '2023'), \n",
       "    ('eprint', '2303.08774'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('OpenAI')])]))), ('openai2022chatgpt', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'ChatGT: Optimizing language models for dialogue'), \n",
       "    ('year', '2022'), \n",
       "    ('url', 'https://openai.com/blog/chatgpt/')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('OpenAI')])]))), ('brown2020language', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Language Models are Few-Shot Learners'), \n",
       "    ('year', '2020'), \n",
       "    ('eprint', '2005.14165'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Brown, Tom B.'), Person('Mann, Benjamin'), Person('Ryder, Nick'), Person('Subbiah, Melanie'), Person('Kaplan, Jared'), Person('Dhariwal, Prafulla'), Person('Neelakantan, Arvind'), Person('Shyam, Pranav'), Person('Sastry, Girish'), Person('Askell, Amanda'), Person('Agarwal, Sandhini'), Person('Herbert-Voss, Ariel'), Person('Krueger, Gretchen'), Person('Henighan, Tom'), Person('Child, Rewon'), Person('Ramesh, Aditya'), Person('Ziegler, Daniel M.'), Person('Wu, Jeffrey'), Person('Winter, Clemens'), Person('Hesse, Christopher'), Person('Chen, Mark'), Person('Sigler, Eric'), Person('Litwin, Mateusz'), Person('Gray, Scott'), Person('Chess, Benjamin'), Person('Clark, Jack'), Person('Berner, Christopher'), Person('McCandlish, Sam'), Person('Radford, Alec'), Person('Sutskever, Ilya'), Person('Amodei, Dario')])]))), ('anthropic2022claude', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Introducing Claude'), \n",
       "    ('year', '2022'), \n",
       "    ('url', 'https://www.anthropic.com/index/introducing-claude')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Anthropic')])]))), ('hendrickx-etal-2013-semeval', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{S}em{E}val-2013 Task 4: Free Paraphrases of Noun Compounds'), \n",
       "    ('booktitle', 'Second Joint Conference on Lexical and Computational Semantics (*{SEM}), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation ({S}em{E}val 2013)'), \n",
       "    ('month', 'June'), \n",
       "    ('year', '2013'), \n",
       "    ('address', 'Atlanta, Georgia, USA'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/S13-2025'), \n",
       "    ('pages', '138--143')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Hendrickx, Iris'), Person('Kozareva, Zornitsa'), Person('Nakov, Preslav'), Person(\"{\\\\'O} S{\\\\'e}aghdha, Diarmuid\"), Person('Szpakowicz, Stan'), Person('Veale, Tony')]), ('editor', [Person('Manandhar, Suresh'), Person('Yuret, Deniz')])]))), ('butnariu-etal-2009-semeval', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{S}em{E}val-2010 Task 9: The Interpretation of Noun Compounds Using Paraphrasing Verbs and Prepositions'), \n",
       "    ('booktitle', 'Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions ({SEW}-2009)'), \n",
       "    ('month', 'June'), \n",
       "    ('year', '2009'), \n",
       "    ('address', 'Boulder, Colorado'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/W09-2416'), \n",
       "    ('pages', '100--105')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Butnariu, Cristina'), Person('Kim, Su Nam'), Person('Nakov, Preslav'), Person(\"{\\\\'O} S{\\\\'e}aghdha, Diarmuid\"), Person('Szpakowicz, Stan'), Person('Veale, Tony')]), ('editor', [Person('Agirre, Eneko'), Person(\"M{\\\\`a}rquez, Llu{\\\\'\\\\i}s\"), Person('Wicentowski, Richard')])]))), ('vecchi2017', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Spicy Adjectives and Nominal Donkeys: Capturing Semantic Deviance Using Compositionality in Distributional Spaces'), \n",
       "    ('journal', 'Cognitive Science'), \n",
       "    ('volume', '41'), \n",
       "    ('number', '1'), \n",
       "    ('pages', '102-136'), \n",
       "    ('keywords', 'Distributional models, Semantic spaces, Compositionality, Meaning representation, Semantic deviance'), \n",
       "    ('doi', 'https://doi.org/10.1111/cogs.12330'), \n",
       "    ('url', 'https://onlinelibrary.wiley.com/doi/abs/10.1111/cogs.12330'), \n",
       "    ('eprint', 'https://onlinelibrary.wiley.com/doi/pdf/10.1111/cogs.12330'), \n",
       "    ('abstract', 'Abstract Sophisticated senator and legislative onion. Whether or not you have ever heard of these things, we all have some intuition that one of them makes much less sense than the other. In this paper, we introduce a large dataset of human judgments about novel adjective-noun phrases. We use these data to test an approach to semantic deviance based on phrase representations derived with compositional distributional semantic methods, that is, methods that derive word meanings from contextual information, and approximate phrase meanings by combining word meanings. We present several simple measures extracted from distributional representations of words and phrases, and we show that they have a significant impact on predicting the acceptability of novel adjective-noun phrases even when a number of alternative measures classically employed in studies of compound processing and bigram plausibility are taken into account. Our results show that the extent to which an attributive adjective alters the distributional representation of the noun is the most significant factor in modeling the distinction between acceptable and deviant phrases. Our study extends current applications of compositional distributional semantic methods to linguistically and cognitively interesting problems, and it offers a new, quantitatively precise approach to the challenge of predicting when humans will find novel linguistic expressions acceptable and when they will not.'), \n",
       "    ('year', '2017')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Vecchi, Eva M.'), Person('Marelli, Marco'), Person('Zamparelli, Roberto'), Person('Baroni, Marco')])]))), ('jiayang-etal-2023-storyanalogy', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{S}tory{A}nalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding'), \n",
       "    ('booktitle', 'Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing'), \n",
       "    ('month', 'December'), \n",
       "    ('year', '2023'), \n",
       "    ('address', 'Singapore'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2023.emnlp-main.706'), \n",
       "    ('doi', '10.18653/v1/2023.emnlp-main.706'), \n",
       "    ('pages', '11518--11537'), \n",
       "    ('abstract', 'Analogy-making between narratives is crucial for human reasoning. In this paper, we evaluate the ability to identify and generate analogies by constructing a first-of-its-kind large-scale story-level analogy corpus, StoryAnalogy, which contains 24K story pairs from diverse domains with human annotations on two similarities from the extended Structure-Mapping Theory. We design a set of tests on StoryAnalogy, presenting the first evaluation of story-level analogy identification and generation. Interestingly, we find that the analogy identification tasks are incredibly difficult not only for sentence embedding models but also for the recent large language models (LLMs) such as ChatGPT and LLaMa. ChatGPT, for example, only achieved around 30{\\\\%} accuracy in multiple-choice questions (compared to over 85{\\\\%} accuracy for humans). Furthermore, we observe that the data in StoryAnalogy can improve the quality of analogy generation in LLMs, where a fine-tuned FlanT5-xxl model achieves comparable performance to zero-shot ChatGPT.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Jiayang, Cheng'), Person('Qiu, Lin'), Person('Chan, Tsz'), Person('Fang, Tianqing'), Person('Wang, Weiqi'), Person('Chan, Chunkit'), Person('Ru, Dongyu'), Person('Guo, Qipeng'), Person('Zhang, Hongming'), Person('Song, Yangqiu'), Person('Zhang, Yue'), Person('Zhang, Zheng')]), ('editor', [Person('Bouamor, Houda'), Person('Pino, Juan'), Person('Bali, Kalika')])]))), ('nagarajah2022understandingnarrativesdimensionsanalogy', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Understanding Narratives through Dimensions of Analogy'), \n",
       "    ('year', '2022'), \n",
       "    ('eprint', '2206.07167'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.AI'), \n",
       "    ('url', 'https://arxiv.org/abs/2206.07167')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Nagarajah, Thiloshon'), Person('Ilievski, Filip'), Person('Pujara, Jay')])]))), ('bitton2022vasrvisualanalogiessituation', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'VASR: Visual Analogies of Situation Recognition'), \n",
       "    ('year', '2022'), \n",
       "    ('eprint', '2212.04542'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CV'), \n",
       "    ('url', 'https://arxiv.org/abs/2212.04542')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Bitton, Yonatan'), Person('Yosef, Ron'), Person('Strugo, Eli'), Person('Shahaf, Dafna'), Person('Schwartz, Roy'), Person('Stanovsky, Gabriel')])]))), ('gladkova-etal-2016-analogy', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', \"Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn{'}t.\"), \n",
       "    ('booktitle', 'Proceedings of the {NAACL} Student Research Workshop'), \n",
       "    ('month', 'June'), \n",
       "    ('year', '2016'), \n",
       "    ('address', 'San Diego, California'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/N16-2002'), \n",
       "    ('doi', '10.18653/v1/N16-2002'), \n",
       "    ('pages', '8--15')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Gladkova, Anna'), Person('Drozd, Aleksandr'), Person('Matsuoka, Satoshi')]), ('editor', [Person('Andreas, Jacob'), Person('Choi, Eunsol'), Person('Lazaridou, Angeliki')])]))), ('czinczoll-etal-2022-scientific', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Scientific and Creative Analogies in Pretrained Language Models'), \n",
       "    ('booktitle', 'Findings of the Association for Computational Linguistics: EMNLP 2022'), \n",
       "    ('month', 'December'), \n",
       "    ('year', '2022'), \n",
       "    ('address', 'Abu Dhabi, United Arab Emirates'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2022.findings-emnlp.153'), \n",
       "    ('doi', '10.18653/v1/2022.findings-emnlp.153'), \n",
       "    ('pages', '2094--2100'), \n",
       "    ('abstract', 'This paper examines the encoding of analogy in large-scale pretrained language models, such as BERT and GPT-2. Existing analogy datasets typically focus on a limited set of analogical relations, with a high similarity of the two domains between which the analogy holds. As a more realistic setup, we introduce the Scientific and Creative Analogy dataset (SCAN), a novel analogy dataset containing systematic mappings of multiple attributes and relational structures across dissimilar domains. Using this dataset, we test the analogical reasoning capabilities of several widely-used pretrained language models (LMs). We find that state-of-the-art LMs achieve low performance on these complex analogy tasks, highlighting the challenges still posed by analogy understanding.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Czinczoll, Tamara'), Person('Yannakoudakis, Helen'), Person('Mishra, Pushkar'), Person('Shutova, Ekaterina')]), ('editor', [Person('Goldberg, Yoav'), Person('Kozareva, Zornitsa'), Person('Zhang, Yue')])]))), ('sultan-shahaf-2022-life', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Life is a Circus and We are the Clowns: Automatically Finding Analogies between Situations and Processes'), \n",
       "    ('booktitle', 'Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing'), \n",
       "    ('month', 'December'), \n",
       "    ('year', '2022'), \n",
       "    ('address', 'Abu Dhabi, United Arab Emirates'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2022.emnlp-main.232'), \n",
       "    ('doi', '10.18653/v1/2022.emnlp-main.232'), \n",
       "    ('pages', '3547--3562'), \n",
       "    ('abstract', 'Analogy-making gives rise to reasoning, abstraction, flexible categorization and counterfactual inference {--} abilities lacking in even the best AI systems today. Much research has suggested that analogies are key to non-brittle systems that can adapt to new domains. Despite their importance, analogies received little attention in the NLP community, with most research focusing on simple word analogies. Work that tackled more complex analogies relied heavily on manually constructed, hard-to-scale input representations.In this work, we explore a more realistic, challenging setup: our input is a pair of natural language procedural texts, describing a situation or a process (e.g., how the heart works/how a pump works). Our goal is to automatically extract entities and their relations from the text and find a mapping between the different domains based on relational similarity (e.g., blood is mapped to water). We develop an interpretable, scalable algorithm and demonstrate that it identifies the correct mappings 87{\\\\%} of the time for procedural texts and 94{\\\\%} for stories from cognitive-psychology literature. We show it can extract analogies from a large dataset of procedural texts, achieving 79{\\\\%} precision (analogy prevalence in data: 3{\\\\%}). Lastly, we demonstrate that our algorithm is robust to paraphrasing the input texts')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Sultan, Oren'), Person('Shahaf, Dafna')]), ('editor', [Person('Goldberg, Yoav'), Person('Kozareva, Zornitsa'), Person('Zhang, Yue')])]))), ('zhu-de-melo-2020-sentence', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Sentence Analogies: Linguistic Regularities in Sentence Embeddings'), \n",
       "    ('booktitle', 'Proceedings of the 28th International Conference on Computational Linguistics'), \n",
       "    ('month', 'December'), \n",
       "    ('year', '2020'), \n",
       "    ('address', 'Barcelona, Spain (Online)'), \n",
       "    ('publisher', 'International Committee on Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2020.coling-main.300'), \n",
       "    ('doi', '10.18653/v1/2020.coling-main.300'), \n",
       "    ('pages', '3389--3400'), \n",
       "    ('abstract', 'While important properties of word vector representations have been studied extensively, far less is known about the properties of sentence vector representations. Word vectors are often evaluated by assessing to what degree they exhibit regularities with regard to relationships of the sort considered in word analogies. In this paper, we investigate to what extent commonly used sentence vector representation spaces as well reflect certain kinds of regularities. We propose a number of schemes to induce evaluation data, based on lexical analogy data as well as semantic relationships between sentences. Our experiments consider a wide range of sentence embedding methods, including ones based on BERT-style contextual embeddings. We find that different models differ substantially in their ability to reflect such regularities.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Zhu, Xunjie'), Person('de Melo, Gerard')]), ('editor', [Person('Scott, Donia'), Person('Bel, Nuria'), Person('Zong, Chengqing')])]))), ('ushio-etal-2021-bert', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{BERT} is to {NLP} what {A}lex{N}et is to {CV}: Can Pre-Trained Language Models Identify Analogies?'), \n",
       "    ('booktitle', 'Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)'), \n",
       "    ('month', 'August'), \n",
       "    ('year', '2021'), \n",
       "    ('address', 'Online'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2021.acl-long.280'), \n",
       "    ('doi', '10.18653/v1/2021.acl-long.280'), \n",
       "    ('pages', '3609--3624'), \n",
       "    ('abstract', \"Analogies play a central role in human commonsense reasoning. The ability to recognize analogies such as {``}eye is to seeing what ear is to hearing{''}, sometimes referred to as analogical proportions, shape how we structure knowledge and understand language. Surprisingly, however, the task of identifying such analogies has not yet received much attention in the language model era. In this paper, we analyze the capabilities of transformer-based language models on this unsupervised task, using benchmarks obtained from educational settings, as well as more commonly used datasets. We find that off-the-shelf language models can identify analogies to a certain extent, but struggle with abstract and complex relations, and results are highly sensitive to model architecture and hyperparameters. Overall the best results were obtained with GPT-2 and RoBERTa, while configurations using BERT were not able to outperform word embedding models. Our results raise important questions for future work about how, and to what extent, pre-trained language models capture knowledge about abstract semantic relations.\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Ushio, Asahi'), Person('Espinosa Anke, Luis'), Person('Schockaert, Steven'), Person('Camacho-Collados, Jose')]), ('editor', [Person('Zong, Chengqing'), Person('Xia, Fei'), Person('Li, Wenjie'), Person('Navigli, Roberto')])]))), ('Petersen2023CanLM', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Can language models learn analogical reasoning? Investigating training objectives and comparisons to human performance'), \n",
       "    ('booktitle', 'Conference on Empirical Methods in Natural Language Processing'), \n",
       "    ('year', '2023'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:263828777')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Petersen, Molly R.'), Person('van der Plas, Lonneke')])]))), ('NIPS2013_9aa42b31', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('bo\n",
       "    oktitle', 'Advances in Neural Information Processing Systems'), \n",
       "    ('pages', ''), \n",
       "    ('publisher', 'Curran Associates, Inc.'), ('title', 'Distributed Representations of Words and Phrases and their Compositionality'), \n",
       "    ('url', 'https://proceedings.neurips.cc/paper_files/paper/2013/file/9aa42b31882ec039965f3c4923ce901b-Paper.pdf'), \n",
       "    ('volume', '26'), \n",
       "    ('year', '2013')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Mikolov, Tomas'), Person('Sutskever, Ilya'), Person('Chen, Kai'), Person('Corrado, Greg S'), Person('Dean, Jeff')]), ('editor', [Person('Burges, C.J.'), Person('Bottou, L.'), Person('Welling, M.'), Person('Ghahramani, Z.'), Person('Weinberger, K.Q.')])]))), ('Hu2023InContextAR', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'In-Context Analogical Reasoning with Pre-Trained Language Models'), \n",
       "    ('booktitle', 'Annual Meeting of the Association for Computational Linguistics'), \n",
       "    ('year', '2023'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:258959097')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Hu, Xiaoyang'), Person('Storks, Shane'), Person('Lewis, Richard L.'), Person('Chai, Joyce Yue')])]))), ('Yasunaga2023LargeLM', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Large Language Models as Analogical Reasoners'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2023'), \n",
       "    ('volume', 'abs/2310.01714'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:263608847')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Yasunaga, Michihiro'), Person('Chen, Xinyun'), Person('Li, Yujia'), Person('Pasupat, Panupong'), Person('Leskovec, Jure'), Person('Liang, Percy'), Person('Chi, Ed Huai-hsin'), Person('Zhou, Denny')])]))), ('Lewis2024UsingCT', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Using Counterfactual Tasks to Evaluate the Generality of Analogical Reasoning in Large Language Models'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2024'), \n",
       "    ('volume', 'abs/2402.08955'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:267657861')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Lewis, Martha'), Person('Mitchell, Melanie')])]))), ('Opielka2024DoLL', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Do Large Language Models Solve ARC Visual Analogies Like People Do?'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2024'), \n",
       "    ('volume', 'abs/2403.09734'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:268510601')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Opielka, Gustaw'), Person('Rosenbusch, Hannes'), Person('Vijverberg, Veerle'), Person('Stevenson, Claire E.')])]))), ('Musker2024SemanticSI', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Semantic Structure-Mapping in LLM and Human Analogical Reasoning'), \n",
       "    ('year', '2024'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:270619799')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Musker, Sam'), Person('Duchnowski, Alex'), Person('Milliere, Raphael'), Person('Pavlick, Ellie')])]))), ('Marquer2022TransferringLM', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Transferring Learned Models of Morphological Analogy'), \n",
       "    ('year', '2022'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:258688324')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Marquer, Esteban'), Person('Murena, Pierre-Alexandre'), Person('Couceiro, Miguel')])]))), ('Webb2022EmergentAR', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Emergent analogical reasoning in large language models'), \n",
       "    ('journal', 'Nature Human Behaviour'), \n",
       "    ('year', '2022'), \n",
       "    ('volume', '7'), \n",
       "    ('pages', '1526 - 1541'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:254854575')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Webb, Taylor W.'), Person('Holyoak, Keith J.'), Person('Lu, Hongjing')])]))), ('Yuan2023BeneathSS', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure Abduction'), \n",
       "    ('booktitle', 'Conference on Empirical Methods in Natural Language Processing'), \n",
       "    ('year', '2023'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:258833317')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Yuan, Siyu'), Person('Chen, Jiangjie'), Person('Ge, Xuyang'), Person('Xiao, Yanghua'), Person('Yang, Deqing')])]))), ('Wijesiriwardene2023ANALOGICALA', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'ANALOGICAL - A Novel Benchmark for Long Text Analogy Evaluation in Large Language Models'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2023'), \n",
       "    ('volume', 'abs/2305.05050'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:258947824')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Wijesiriwardene, Thilini'), Person('Wickramarachchi, Ruwan'), Person('Gajera, Bimal'), Person('Gowaikar, Shreeyash Mukul'), Person('Gupta, Chandan'), Person('Chadha, Aman'), Person('Reganti, Aishwarya N.'), Person('Sheth, Amit P.'), Person('Das, Amitava')])]))), ('Olson2021NamingUW', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Naming unrelated words predicts creativity'), \n",
       "    ('journal', 'Proceedings of the National Academy of Sciences of the United States of America'), \n",
       "    ('year', '2021'), \n",
       "    ('volume', '118'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:235472442')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Olson, Jay A.'), Person('Nahas, Johnny'), Person('Chmoulevitch, Denis'), Person('Cropper, Simon J.'), Person('Webb, Margaret E.')])]))), ('Chen2023ProbingTC', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Probing the Creativity of Large Language Models: Can models produce divergent semantic association?'), \n",
       "    ('booktitle', 'Conference on Empirical Methods in Natural Language Processing'), \n",
       "    ('year', '2023'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:264172665')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Chen, Honghua'), Person('Ding, Nai')])]))), ('forster2009creat', Entry('article',\n",
       "  fields=[\n",
       "    ('year', '2009'), \n",
       "    ('month', '01'), \n",
       "    ('pages', ''), \n",
       "    ('title', 'Creativity Evaluation through Latent Semantic Analysis'), \n",
       "    ('journal', 'Proceedings of the Annual Conference of the Cognitive Science Society')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Forster, Eve'), Person('Ca, Eve'), Person('Dunbar, Kevin'), Person('Ca, Dunbar@utsc')])]))), ('heinen2017semantic', Entry('article',\n",
       "  fields=[\n",
       "    ('year', '2017'), \n",
       "    ('month', '04'), \n",
       "    ('pages', ''), \n",
       "    ('title', 'Semantic Distance: An Automated Measure of Creativity That Is Novel and Appropriate'), \n",
       "    ('volume', '12'), \n",
       "    ('journal', 'Psychology of Aesthetics, Creativity, and the Arts'), \n",
       "    ('doi', '10.1037/aca0000125')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Heinen, David'), Person('Johnson, Dan')])]))), ('Franceschelli2022DeepCreativityMC', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'DeepCreativity: Measuring Creativity with Deep Learning Techniques'), \n",
       "    ('journal', 'Intelligenza Artificiale'), \n",
       "    ('year', '2022'), \n",
       "    ('volume', '16'), \n",
       "    ('pages', '151-163'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:246015719')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Franceschelli, Giorgio'), Person('Musolesi, Mirco')])]))), ('goodfellow2014generative', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Generative Adversarial Networks'), \n",
       "    ('year', '2014'), \n",
       "    ('eprint', '1406.2661'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'stat.ML')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Goodfellow, Ian J.'), Person('Pouget-Abadie, Jean'), Person('Mirza, Mehdi'), Person('Xu, Bing'), Person('Warde-Farley, David'), Person('Ozair, Sherjil'), Person('Courville, Aaron'), Person('Bengio, Yoshua')])]))), ('Baldi2010OfBA', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Of bits and wows: A Bayesian theory of surprise with applications to attention'), \n",
       "    ('journal', 'Neural networks : the official journal of the International Neural Network Society'), \n",
       "    ('year', '2010'), \n",
       "    ('volume', '23 5'), \n",
       "    ('pages', '649-66'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:2070585')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Baldi, Pierre'), Person('Itti, Laurent')])]))), ('Hale2006UncertaintyAT', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Uncertainty About the Rest of the Sentence'), \n",
       "    ('journal', 'Cognitive science'), \n",
       "    ('year', '2006'), \n",
       "    ('volume', '30 4'), \n",
       "    ('pages', '643-72'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:15922633')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Hale, John')])]))), ('Frank2010UncertaintyRA', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Uncertainty Reduction as a Measure of Cognitive Processing Effort'), \n",
       "    ('booktitle', 'CMCL@ACL'), \n",
       "    ('year', '2010'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:3097133')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Frank, S.')])]))), ('Grace2014WhatTE', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', \"What to expect when you're expecting: The role of unexpectedness in computationally evaluating creativity\"), \n",
       "    ('booktitle', 'International Conference on Innovative Computing and Cloud Computing'), \n",
       "    ('year', '2014'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:18330744')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Grace, Kazjon'), Person('Maher, Mary Lou')])]))), ('xie-etal-2023-deltascore', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{D}elta{S}core: Fine-Grained Story Evaluation with Perturbations'), \n",
       "    ('booktitle', 'Findings of the Association for Computational Linguistics: EMNLP 2023'), \n",
       "    ('month', 'December'), \n",
       "    ('year', '2023'), \n",
       "    ('address', 'Singapore'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2023.findings-emnlp.353'), \n",
       "    ('doi', '10.18653/v1/2023.findings-emnlp.353'), \n",
       "    ('pages', '5317--5331'), \n",
       "    ('abstract', 'Numerous evaluation metrics have been developed for natural language generation tasks, but their effectiveness in evaluating stories is limited as they are not specifically tailored to assess intricate aspects of storytelling, such as fluency and interestingness. In this paper, we introduce DeltaScore, a novel methodology that uses perturbation techniques for the evaluation of nuanced story aspects. We posit that the extent to which a story excels in a specific aspect (e.g., fluency) correlates with the magnitude of its susceptibility to particular perturbations (e.g., the introduction of typos). Given this, we measure the quality of an aspect by calculating the likelihood difference between pre- and post-perturbation states using pre-trained language models. We compare DeltaScore with existing metrics on storytelling datasets from two domains in five fine-grained story aspects: fluency, coherence, relatedness, logicality, and interestingness. DeltaScore demonstrates strong performance, revealing a surprising finding that one specific perturbation proves highly effective in capturing multiple aspects. Source code is available on our GitHub repository.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Xie, Zhuohan'), Person('Li, Miao'), Person('Cohn, Trevor'), Person('Lau, Jey')]), ('editor', [Person('Bouamor, Houda'), Person('Pino, Juan'), Person('Bali, Kalika')])]))), ('vaswani2017attention', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Attention Is All You Need'), \n",
       "    ('year', '2017'), \n",
       "    ('eprint', '1706.03762'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Vaswani, Ashish'), Person('Shazeer, Noam'), Person('Parmar, Niki'), Person('Uszkoreit, Jakob'), Person('Jones, Llion'), Person('Gomez, Aidan N.'), Person('Kaiser, Lukasz'), Person('Polosukhin, Illia')])]))), ('Yu2020HowNS', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'How nouns surface as verbs: Inference and generation in word class conversion'), \n",
       "    ('booktitle', 'Annual Meeting of the Cognitive Science Society'), \n",
       "    ('year', '2020'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:221020946')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Yu, Lei'), Person('Sanyoura, Lana El'), Person('Xu, Yang')])]))), ('watson2021coin', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Coin it up: generalization of creative constructions in the wild'), \n",
       "    ('booktitle', 'Proceedings of the Annual Meeting of the Cognitive Science Society'), \n",
       "    ('year', '2021')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Watson, Julia'), Person('Samir, Farhan'), Person('Stevenson, Suzanne'), Person('Beekhuizen, Barend')])]))), ('fowler1986linguistic', Entry('book',\n",
       "  fields=[\n",
       "    ('title', 'Linguistic Criticism'), \n",
       "    ('isbn', '9780192191250'), \n",
       "    ('lccn', '85015418'), \n",
       "    ('series', 'An OPUS book'), \n",
       "    ('url', 'https://books.google.ch/books?id=0vm2AAAAIAAJ'), \n",
       "    ('year', '1986'), \n",
       "    ('publisher', 'Oxford University Press')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Fowler, R.')])]))), ('skalicky2022fork', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', '“A fork is a food stabber”: Linguistic creativity in English L1 and L2 speakers'), \n",
       "    ('year', '2022'), \n",
       "    ('publisher', 'Proceedings of the Annual Meeting of the Cognitive Science Society')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Skalicky, Stephen'), Person('Bell, Nancy'), Person('Dascalu, Mihai'), Person('Crossley, Scott')])]))), ('Brennan1996ConceptualPA', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Conceptual pacts and lexical choice in conversation.'), \n",
       "    ('journal', 'Journal of experimental psychology. Learning, memory, and cognition'), \n",
       "    ('year', '1996'), \n",
       "    ('volume', '22 6'), \n",
       "    ('pages', '1482-93'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:15405799')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Brennan, Susan'), Person('Clark, Herbert H.')])]))), ('liu-etal-2023-vera', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements'), \n",
       "    ('booktitle', 'Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing'), \n",
       "    ('month', 'December'), \n",
       "    ('year', '2023'), \n",
       "    ('address', 'Singapore'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2023.emnlp-main.81'), \n",
       "    ('doi', '10.18653/v1/2023.emnlp-main.81'), \n",
       "    ('pages', '1264--1287'), \n",
       "    ('abstract', \"Today{'}s language models can be remarkably intelligent yet still produce text that contains trivial commonsense errors. Therefore, we seek a retrospective verification approach that can reflect on the commonsense plausibility of the machine text, and introduce Vera, a general-purpose model that learns to estimate the commonsense plausibility of declarative statements. To support diverse commonsense domains, Vera is trained on {\\\\textasciitilde}7M commonsense statements that are automatically converted from 19 QA datasets and two commonsense knowledge bases, and using a combination of three training objectives. When applied to solving commonsense problems in the verification format, Vera substantially outperforms existing models that can be repurposed for commonsense verification, even including GPT-3.5/ChatGPT/GPT-4, and it further exhibits generalization capabilities to unseen tasks and provides well-calibrated outputs. We find that Vera excels at filtering machine-generated commonsense knowledge and is useful in detecting erroneous commonsense statements generated by models like ChatGPT in real-world settings.\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Liu, Jiacheng'), Person('Wang, Wenya'), Person('Wang, Dianzhuo'), Person('Smith, Noah'), Person('Choi, Yejin'), Person('Hajishirzi, Hannaneh')]), ('editor', [Person('Bouamor, Houda'), Person('Pino, Juan'), Person('Bali, Kalika')])]))), ('ravfogel2023retrieving', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Retrieving Texts based on Abstract Descriptions'), \n",
       "    ('year', '2023'), \n",
       "    ('eprint', '2305.12517'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Ravfogel, Shauli'), Person('Pyatkin, Valentina'), Person('Cohen, Amir DN'), Person('Manevich, Avshalom'), Person('Goldberg, Yoav')])]))), ('Zhu2009HowCI', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'How creative is your writing? A linguistic creativity measure from computer science and cognitive psychology perspectives'), \n",
       "    ('year', '2009'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:17340001')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Zhu, Xiaojin'), Person('Xu, Zhiting'), Person('Khot, Tushar')])]))), ('maguire2004evidence', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('year', '2004'), \n",
       "    ('month', '08'), \n",
       "    ('pages', ''), \n",
       "    ('title', 'Evidence of muddy knowledge in reaching for the stars: Creating novel endings to event sequences.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Maguire, Rebecca')])]))), ('Lynott2005FamiliarityAC', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Familiarity and creativity in novel compound production.'), \n",
       "    ('year', '2005'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:9947884')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Lynott, Dermot'), Person('Bara, Bruno G.'), Person('Barsalou, Lawrence W.'), Person('Bucciarelli, Monica')])]))), ('smith1995creative', Entry('book',\n",
       "  fields=[\n",
       "    ('title', 'The Creative Cognition Approach'), \n",
       "    ('isbn', '9780262193542'), \n",
       "    ('lccn', 'lc94021947'), \n",
       "    ('series', 'Bradford book'), \n",
       "    ('url', 'https://books.google.ch/books?id=iEMWI4Z1rF8C'), \n",
       "    ('year', '1995'), \n",
       "    ('publisher', 'BRADFORD BOOK')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Smith, S.M.'), Person('Ward, T.B.'), Person('Finke, R.A.')])]))), ('li2023large', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Large Language Models Understand and Can be Enhanced by Emotional Stimuli'), \n",
       "    ('year', '2023'), \n",
       "    ('eprint', '2307.11760'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Li, Cheng'), Person('Wang, Jindong'), Person('Zhang, Yixuan'), Person('Zhu, Kaijie'), Person('Hou, Wenxin'), Person('Lian, Jianxun'), Person('Luo, Fang'), Person('Yang, Qiang'), Person('Xie, Xing')])]))), ('SummersStay2023BrainstormTS', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Brainstorm, then Select: a Generative Language Model Improves Its Creativity Score'), \n",
       "    ('year', '2023'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:259305709')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Summers-Stay, Douglas'), Person('Lukin, Stephanie M.'), Person('Voss, Clare R.')])]))), ('riley2019evidence', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Evidence that Threatening Situations Enhance Creativity'), \n",
       "    ('year', '2019'), \n",
       "    ('eprint', '1308.4245'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'q-bio.NC')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Riley, Sean N.'), Person('Gabora, Liane')])]))), ('Liu2020FeelingOC', Entry('article',\n",
       "  fields=[\n",
       "    ('title', \"Feeling of Competence Affects Children's Curiosity and Creativity\"), \n",
       "    ('journal', 'Cognitive Science'), \n",
       "    ('year', '2020'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:220039894')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Liu, Rongzhi'), Person('Xu, Fei')])]))), ('beck2023psych', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', \"Psychological distance and children's innovative problem solving\"), \n",
       "    ('year', '2023'), \n",
       "    ('journal', 'Cognitive Science')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Beck, Sarah R'), Person('Whalley, Clare Louise'), Person('Lagaha, Sabbi'), Person('APPERLY, Ian'), Person('Chappell, Jackie M'), Person('Taylor Bunce, Louise')])]))), ('meister2023locally', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Locally Typical Sampling'), \n",
       "    ('year', '2023'), \n",
       "    ('eprint', '2202.00666'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Meister, Clara'), Person('Pimentel, Tiago'), Person('Wiher, Gian'), Person('Cotterell, Ryan')])]))), ('li2023contrastive', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Contrastive Decoding: Open-ended Text Generation as Optimization'), \n",
       "    ('year', '2023'), \n",
       "    ('eprint', '2210.15097'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Li, Xiang Lisa'), Person('Holtzman, Ari'), Person('Fried, Daniel'), Person('Liang, Percy'), Person('Eisner, Jason'), Person('Hashimoto, Tatsunori'), Person('Zettlemoyer, Luke'), Person('Lewis, Mike')])]))), ('Guimaraes2017ObjectiveReinforcedGA', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2017'), \n",
       "    ('volume', 'abs/1705.10843'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:35911567')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Guimaraes, Gabriel Lima'), Person(\"S{\\\\'a}nchez-Lengeling, Benjam{\\\\'i}n\"), Person('Farias, Pedro Luis Cunha'), Person(\"Aspuru-Guzik, Al{\\\\'a}n\")])]))), ('Broad2021ActiveDW', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Active Divergence with Generative Deep Learning - A Survey and Taxonomy'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2021'), \n",
       "    ('volume', 'abs/2107.05599'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:235794880')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Broad, Terence'), Person('Berns, Sebastian'), Person('Colton, Simon'), Person('Grierson, Mick')])]))), ('Bunescu2019LearningTS', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Learning to Surprise: A Composer-Audience Architecture'), \n",
       "    ('booktitle', 'International Conference on Innovative Computing and Cloud Computing'), \n",
       "    ('year', '2019'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:218606180')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Bunescu, Razvan C.'), Person('Uduehi, Oseremen O.')])]))), ('Franceschelli2023OnTC', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'On the Creativity of Large Language Models'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2023'), \n",
       "    ('volume', 'abs/2304.00008'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:257913327')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Franceschelli, Giorgio'), Person('Musolesi, Mirco')])]))), ('kuznetsova-etal-2013-understanding', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Understanding and Quantifying Creativity in Lexical Composition'), \n",
       "    ('booktitle', 'Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing'), \n",
       "    ('month', 'October'), \n",
       "    ('year', '2013'), \n",
       "    ('address', 'Seattle, Washington, USA'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/D13-1124'), \n",
       "    ('pages', '1246--1258')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Kuznetsova, Polina'), Person('Chen, Jianfu'), Person('Choi, Yejin')]), ('editor', [Person('Yarowsky, David'), Person('Baldwin, Timothy'), Person('Korhonen, Anna'), Person('Livescu, Karen'), Person('Bethard, Steven')])]))), ('bunescu-uduehi-2022-distribution', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Distribution-Based Measures of Surprise for Creative Language: Experiments with Humor and Metaphor'), \n",
       "    ('booktitle', 'Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)'), \n",
       "    ('month', 'December'), \n",
       "    ('year', '2022'), \n",
       "    ('address', 'Abu Dhabi, United Arab Emirates (Hybrid)'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2022.flp-1.10'), \n",
       "    ('doi', '10.18653/v1/2022.flp-1.10'), \n",
       "    ('pages', '68--78'), \n",
       "    ('abstract', \"Novelty or surprise is a fundamental attribute of creative output. As such, we postulate that a writer{'}s creative use of language leads to word choices and, more importantly, corresponding semantic structures that are unexpected for the reader. In this paper we investigate measures of surprise that rely solely on word distributions computed by language models and show empirically that creative language such as humor and metaphor is strongly correlated with surprise. Surprisingly at first, information content is observed to be at least as good a predictor of creative language as any of the surprise measures investigated. However, the best prediction performance is obtained when information and surprise measures are combined, showing that surprise measures capture an aspect of creative language that goes beyond information content.\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Bunescu, Razvan C.'), Person('Uduehi, Oseremen O.')]), ('editor', [Person('Ghosh, Debanjan'), Person('Beigman Klebanov, Beata'), Person('Muresan, Smaranda'), Person('Feldman, Anna'), Person('Poria, Soujanya'), Person('Chakrabarty, Tuhin')])]))), ('he-etal-2023-hauser', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{HAUSER}: Towards Holistic and Automatic Evaluation of Simile Generation'), \n",
       "    ('booktitle', 'Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)'), \n",
       "    ('month', 'July'), \n",
       "    ('year', '2023'), \n",
       "    ('address', 'Toronto, Canada'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2023.acl-long.702'), \n",
       "    ('doi', '10.18653/v1/2023.acl-long.702'), \n",
       "    ('pages', '12557--12572'), \n",
       "    ('abstract', 'Similes play an imperative role in creative writing such as story and dialogue generation. Proper evaluation metrics are like a beacon guiding the research of simile generation (SG). However, it remains under-explored as to what criteria should be considered, how to quantify each criterion into metrics, and whether the metrics are effective for comprehensive, efficient, and reliable SG evaluation. To address the issues, we establish HAUSER, a holistic and automatic evaluation system for the SG task, which consists of five criteria from three perspectives and automatic metrics for each criterion. Through extensive experiments, we verify that our metrics are significantly more correlated with human ratings from each perspective compared with prior automatic metrics. Resources of HAUSER are publicly available at \\\\url{https://github.com/Abbey4799/HAUSER}.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('He, Qianyu'), Person('Zhang, Yikai'), Person('Liang, Jiaqing'), Person('Huang, Yuncheng'), Person('Xiao, Yanghua'), Person('Chen, Yunwen')]), ('editor', [Person('Rogers, Anna'), Person('Boyd-Graber, Jordan'), Person('Okazaki, Naoaki')])]))), ('Kim2006CanWT', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Can We Trust Creativity Tests? A Review of the Torrance Tests of Creative Thinking (TTCT)'), \n",
       "    ('journal', 'Creativity Research Journal'), \n",
       "    ('year', '2006'), \n",
       "    ('volume', '18'), \n",
       "    ('pages', '14 - 3'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:17636888')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Kim, Kyung H')])]))), ('Amabile1983TheSP', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'The social psychology of creativity: A componential conceptualization.'), \n",
       "    ('journal', 'Journal of Personality and Social Psychology'), \n",
       "    ('year', '1983'), \n",
       "    ('volume', '45'), \n",
       "    ('pages', '357-376'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:145719543')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Amabile, T. M.')])]))), ('Prabhakaran2013ThinSO', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Thin slices of creativity: Using single-word utterances to assess creative cognition'), \n",
       "    ('journal', 'Behavior Research Methods'), \n",
       "    ('year', '2013'), \n",
       "    ('volume', '46'), \n",
       "    ('pages', '641 - 659'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:2159817')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Prabhakaran, Ranjani'), Person('Green, Adam E.'), Person('Gray, Jeremy R.')])]))), ('Dunbar2009CreativityET', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Creativity Evaluation through Latent Semantic Analysis'), \n",
       "    ('year', '2009'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:18209155')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Dunbar, Kevin'), Person('Forster, Eve')])]))), ('Harbison2014AutomatedSO', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Automated scoring of originality using semantic representations'), \n",
       "    ('journal', 'Cognitive Science'), \n",
       "    ('year', '2014'), \n",
       "    ('volume', '36'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:2645346')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Harbison, J. Isaiah'), Person('Haarmann, Henk J.')])]))), ('Beaty2020AutomatingCA', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Automating creativity assessment with SemDis: An open platform for computing semantic distance'), \n",
       "    ('journal', 'Behavior Research Methods'), \n",
       "    ('year', '2020'), \n",
       "    ('volume', '53'), \n",
       "    ('pages', '757 - 780'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:221403286')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Beaty, Roger E.'), Person('Johnson, Dan Richard')])]))), ('Johnson2022DivergentSI', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Divergent semantic integration (DSI): Extracting creativity from narratives with distributional semantic modeling'), \n",
       "    ('journal', 'Behavior Research Methods'), \n",
       "    ('year', '2022'), \n",
       "    ('volume', '55'), \n",
       "    ('pages', '3726 - 3759'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:252969336')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Johnson, Dan Richard'), Person('Kaufman, J.'), Person('Baker, Brendan S.'), Person('Patterson, John D.'), Person('Barbot, Baptiste'), Person('Green, Adam E.'), Person('van Hell, Janet G.'), Person('Kennedy, Evan S.'), Person('Sullivan, Grace F'), Person('Taylor, Christa L.'), Person('Ward, Thomas'), Person('Beaty, Roger E.')])]))), ('amin-burghardt-2020-survey', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'A Survey on Approaches to Computational Humor Generation'), \n",
       "    ('booktitle', 'Proceedings of the 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature'), \n",
       "    ('month', 'December'), \n",
       "    ('year', '2020'), \n",
       "    ('address', 'Online'), \n",
       "    ('publisher', 'International Committee on Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2020.latechclfl-1.4'), \n",
       "    ('pages', '29--41'), \n",
       "    ('abstract', 'We provide a comprehensive overview of existing systems for the computational generation of verbal humor in the form of jokes and short humorous texts. Considering linguistic humor theories, we analyze the systematic strengths and drawbacks of the different approaches. In addition, we show how the systems have been evaluated so far and propose two evaluation criteria: humorousness and complexity. From our analysis of the field, we conclude new directions for the advancement of computational humor generation.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Amin, Miriam'), Person('Burghardt, Manuel')]), ('editor', [Person('DeGaetano, Stefania'), Person('Kazantseva, Anna'), Person('Reiter, Nils'), Person('Szpakowicz, Stan')])]))), ('he-etal-2019-pun', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Pun Generation with Surprise'), \n",
       "    ('booktitle', 'Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)'), \n",
       "    ('month', 'June'), \n",
       "    ('year', '2019'), \n",
       "    ('address', 'Minneapolis, Minnesota'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/N19-1172'), \n",
       "    ('doi', '10.18653/v1/N19-1172'), \n",
       "    ('pages', '1734--1744'), \n",
       "    ('abstract', \"We tackle the problem of generating a pun sentence given a pair of homophones (e.g., {``}died{''} and {``}dyed{''}). Puns are by their very nature statistically anomalous and not amenable to most text generation methods that are supervised by a large corpus. In this paper, we propose an unsupervised approach to pun generation based on lots of raw (unhumorous) text and a surprisal principle. Specifically, we posit that in a pun sentence, there is a strong association between the pun word (e.g., {``}dyed{''}) and the distant context, but a strong association between the alternative word (e.g., {``}died{''}) and the immediate context. We instantiate the surprisal principle in two ways: (i) as a measure based on the ratio of probabilities given by a language model, and (ii) a retrieve-and-edit approach based on words suggested by a skip-gram model. Based on human evaluation, our retrieve-and-edit approach generates puns successfully 30{\\\\%} of the time, doubling the success rate of a neural generation baseline.\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('He, He'), Person('Peng, Nanyun'), Person('Liang, Percy')]), ('editor', [Person('Burstein, Jill'), Person('Doran, Christy'), Person('Solorio, Thamar')])]))), ('mittal-etal-2022-ambipun', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{A}mbi{P}un: Generating Humorous Puns with Ambiguous Context'), \n",
       "    ('booktitle', 'Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies'), \n",
       "    ('month', 'July'), \n",
       "    ('year', '2022'), \n",
       "    ('address', 'Seattle, United States'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2022.naacl-main.77'), \n",
       "    ('doi', '10.18653/v1/2022.naacl-main.77'), \n",
       "    ('pages', '1053--1062'), \n",
       "    ('abstract', 'In this paper, we propose a simple yet effective way to generate pun sentences that does not require any training on existing puns. Our approach is inspired by humor theories that ambiguity comes from the context rather than the pun word itself. Given a pair of definitions of a pun word, our model first produces a list of related concepts through a reverse dictionary. We then utilize one-shot GPT3 to generate context words and then generate puns incorporating context words from both concepts. Human evaluation shows that our method successfully generates pun 52{\\\\%} of the time, outperforming well-crafted baselines and the state-of-the-art models by a large margin.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Mittal, Anirudh'), Person('Tian, Yufei'), Person('Peng, Nanyun')]), ('editor', [Person('Carpuat, Marine'), Person('de Marneffe, Marie-Catherine'), Person('Meza Ruiz, Ivan Vladimir')])]))), ('chakrabarty-etal-2020-r', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{R}{\\\\^{}}3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge'), \n",
       "    ('booktitle', 'Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics'), \n",
       "    ('month', 'July'), \n",
       "    ('year', '2020'), \n",
       "    ('address', 'Online'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2020.acl-main.711'), \n",
       "    ('doi', '10.18653/v1/2020.acl-main.711'), \n",
       "    ('pages', '7976--7986'), \n",
       "    ('abstract', 'We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence. Our method employs a retrieve-and-edit framework to instantiate two major characteristics of sarcasm: reversal of valence and semantic incongruity with the context, which could include shared commonsense or world knowledge between the speaker and the listener. While prior works on sarcasm generation predominantly focus on context incongruity, we show that combining valence reversal and semantic incongruity based on the commonsense knowledge generates sarcasm of higher quality. Human evaluation shows that our system generates sarcasm better than humans 34{\\\\%} of the time, and better than a reinforced hybrid baseline 90{\\\\%} of the time.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Chakrabarty, Tuhin'), Person('Ghosh, Debanjan'), Person('Muresan, Smaranda'), Person('Peng, Nanyun')]), ('editor', [Person('Jurafsky, Dan'), Person('Chai, Joyce'), Person('Schluter, Natalie'), Person('Tetreault, Joel')])]))), ('hessel-etal-2023-androids', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', \"Do Androids Laugh at Electric Sheep? Humor {``}Understanding{''} Benchmarks from The New Yorker Caption Contest\"), \n",
       "    ('booktitle', 'Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)'), \n",
       "    ('month', 'July'), \n",
       "    ('year', '2023'), \n",
       "    ('address', 'Toronto, Canada'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2023.acl-long.41'), \n",
       "    ('doi', '10.18653/v1/2023.acl-long.41'), \n",
       "    ('pages', '688--714'), \n",
       "    ('abstract', \"Large neural networks can now generate jokes, but do they really {``}understand{''} humor? We challenge AI models with three tasks derived from the New Yorker Cartoon Caption Contest: matching a joke to a cartoon, identifying a winning caption, and explaining why a winning caption is funny. These tasks encapsulate progressively more sophisticated aspects of {``}understanding{''} a cartoon; key elements are the complex, often surprising relationships between images and captions and the frequent inclusion of indirect and playful allusions to human experience and culture. We investigate both multimodal and language-only models: the former are challenged with the cartoon images directly, while the latter are given multifaceted descriptions of the visual scene to simulate human-level visual understanding. We find that both types of models struggle at all three tasks. For example, our best multimodal models fall 30 accuracy points behind human performance on the matching task, and, even when provided ground-truth visual scene descriptors, human-authored explanations are preferred head-to-head over the best machine-authored ones (few-shot GPT-4) in more than 2/3 of cases. We release models, code, leaderboard, and corpus, which includes newly-gathered annotations describing the image{'}s locations/entities, what{'}s unusual in the scene, and an explanation of the joke.\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Hessel, Jack'), Person('Marasovic, Ana'), Person('Hwang, Jena D.'), Person('Lee, Lillian'), Person('Da, Jeff'), Person('Zellers, Rowan'), Person('Mankoff, Robert'), Person('Choi, Yejin')]), ('editor', [Person('Rogers, Anna'), Person('Boyd-Graber, Jordan'), Person('Okazaki, Naoaki')])]))), ('jentzsch-kersting-2023-chatgpt', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{C}hat{GPT} is fun, but it is not funny! Humor is still challenging Large Language Models'), \n",
       "    ('booktitle', 'Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, {\\\\&} Social Media Analysis'), \n",
       "    ('month', 'July'), \n",
       "    ('year', '2023'), \n",
       "    ('address', 'Toronto, Canada'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2023.wassa-1.29'), \n",
       "    ('doi', '10.18653/v1/2023.wassa-1.29'), \n",
       "    ('pages', '325--340'), \n",
       "    ('abstract', \"Humor is a central aspect of human communication that has not been solved for artificial agents so far. Large language models (LLMs) are increasingly able to capture implicit and contextual information. Especially, OpenAI{'}s ChatGPT recently gained immense public attention. The GPT3-based model almost seems to communicate on a human level and can even tell jokes. Humor is an essential component of human communication. But is ChatGPT really funny?We put ChatGPT{'}s sense of humor to the test. In a series of exploratory experiments around jokes, i.e., generation, explanation, and detection, we seek to understand ChatGPT{'}s capability to grasp and reproduce human humor. Since the model itself is not accessible, we applied prompt-based experiments. Our empirical evidence indicates that jokes are not hard-coded but mostly also not newly generated by the model. Over 90{\\\\%} of 1008 generated jokes were the same 25 Jokes. The system accurately explains valid jokes but also comes up with fictional explanations for invalid jokes. Joke-typical characteristics can mislead ChatGPT in the classification of jokes. ChatGPT has not solved computational humor yet but it can be a big leap toward {``}funny{''} machines.\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Jentzsch, Sophie'), Person('Kersting, Kristian')]), ('editor', [Person('Barnes, Jeremy'), Person(\"De Clercq, Orph{\\\\'e}e\"), Person('Klinger, Roman')])]))), ('chakrabarty-etal-2020-generating', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation'), \n",
       "    ('booktitle', 'Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)'), \n",
       "    ('month', 'November'), \n",
       "    ('year', '2020'), \n",
       "    ('address', 'Online'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2020.emnlp-main.524'), \n",
       "    ('doi', '10.18653/v1/2020.emnlp-main.524'), \n",
       "    ('pages', '6455--6469'), \n",
       "    ('abstract', 'Literary tropes, from poetry to stories, are at the crux of human imagination and communication. Figurative language such as a simile go beyond plain expressions to give readers new insights and inspirations. In this paper, we tackle the problem of simile generation. Generating a simile requires proper understanding for effective mapping of properties between two concepts. To this end, we first propose a method to automatically construct a parallel corpus by transforming a large number of similes collected from Reddit to their literal counterpart using structured common sense knowledge. We then propose to fine-tune a pre-trained sequence to sequence model, BART (Lewis et al 2019), on the literal-simile pairs to gain generalizability, so that we can generate novel similes given a literal sentence. Experiments show that our approach generates 88{\\\\%} novel similes that do not share properties with the training data. Human evaluation on an independent set of literal statements shows that our model generates similes better than two literary experts 37{\\\\%} of the time when compared pairwise. We also show how replacing literal sentences with similes from our best model in machine-generated stories improves evocativeness and leads to better acceptance by human judges.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Chakrabarty, Tuhin'), Person('Muresan, Smaranda'), Person('Peng, Nanyun')]), ('editor', [Person('Webber, Bonnie'), Person('Cohn, Trevor'), Person('He, Yulan'), Person('Liu, Yang')])]))), ('Chakrabarty2021ItsNR', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'It’s not Rocket Science: Interpreting Figurative Language in Narratives'), \n",
       "    ('journal', 'Transactions of the Association for Computational Linguistics'), \n",
       "    ('year', '2021'), \n",
       "    ('volume', '10'), \n",
       "    ('pages', '589-606'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:237371845')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Chakrabarty, Tuhin'), Person('Choi, Yejin'), Person('Shwartz, Vered')])]))), ('Chakrabarty2022FLUTEFL', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'FLUTE: Figurative Language Understanding through Textual Explanations'), \n",
       "    ('booktitle', 'Conference on Empirical Methods in Natural Language Processing'), \n",
       "    ('year', '2022'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:252780978')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Chakrabarty, Tuhin'), Person('Saakyan, Arkadiy'), Person('Ghosh, Debanjan'), Person('Muresan, Smaranda')])]))), ('Mohammad2016MetaphorAA', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Metaphor as a Medium for Emotion: An Empirical Study'), \n",
       "    ('booktitle', 'International Workshop on Semantic Evaluation'), \n",
       "    ('year', '2016'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:989439')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Mohammad, Saif M.'), Person('Shutova, Ekaterina'), Person('Turney, Peter D.')])]))), ('Stowe2020MetaphoricPG', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Metaphoric Paraphrase Generation'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2020'), \n",
       "    ('volume', 'abs/2002.12854'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:211572789')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Stowe, Kevin'), Person('Ribeiro, Leonardo'), Person('Gurevych, Iryna')])]))), ('Stowe2021MetaphorGW', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Metaphor Generation with Conceptual Mappings'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2021'), \n",
       "    ('volume', 'abs/2106.01228'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:235294009')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Stowe, Kevin'), Person('Chakrabarty, Tuhin'), Person('Peng, Nanyun'), Person('Muresan, Smaranda'), Person('Gurevych, Iryna')])]))), ('mohler-etal-2016-introducing', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Introducing the {LCC} Metaphor Datasets'), \n",
       "    ('booktitle', \"Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)\"), \n",
       "    ('month', 'May'), \n",
       "    ('year', '2016'), \n",
       "    ('address', 'Portoro{\\\\v{z}}, Slovenia'), \n",
       "    ('publisher', 'European Language Resources Association (ELRA)'), \n",
       "    ('url', 'https://aclanthology.org/L16-1668'), \n",
       "    ('pages', '4221--4227'), \n",
       "    ('abstract', 'In this work, we present the Language Computer Corporation (LCC) annotated metaphor datasets, which represent the largest and most comprehensive resource for metaphor research to date. These datasets were produced over the course of three years by a staff of nine annotators working in four languages (English, Spanish, Russian, and Farsi). As part of these datasets, we provide (1) metaphoricity ratings for within-sentence word pairs on a four-point scale, (2) scored links to our repository of 114 source concept domains and 32 target concept domains, and (3) ratings for the affective polarity and intensity of each pair. Altogether, we provide 188,741 annotations in English (for 80,100 pairs), 159,915 annotations in Spanish (for 63,188 pairs), 99,740 annotations in Russian (for 44,632 pairs), and 137,186 annotations in Farsi (for 57,239 pairs). In addition, we are providing a large set of likely metaphors which have been independently extracted by our two state-of-the-art metaphor detection systems but which have not been analyzed by our team of annotators.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Mohler, Michael'), Person('Brunson, Mary'), Person('Rink, Bryan'), Person('Tomlinson, Marc')]), ('editor', [Person('Calzolari, Nicoletta'), Person('Choukri, Khalid'), Person('Declerck, Thierry'), Person('Goggi, Sara'), Person('Grobelnik, Marko'), Person('Maegaard, Bente'), Person('Mariani, Joseph'), Person('Mazo, Helene'), Person('Moreno, Asuncion'), Person('Odijk, Jan'), Person('Piperidis, Stelios')])]))), ('jacobs2018', Entry('article',\n",
       "  fields=[\n",
       "    ('TITLE', 'The Gutenberg English Poetry Corpus: Exemplary Quantitative Narrative Analyses'), \n",
       "    ('JOURNAL', 'Frontiers in Digital Humanities'), \n",
       "    ('VOLUME', '5'), \n",
       "    ('YEAR', '2018'), \n",
       "    ('URL', 'https://www.frontiersin.org/journals/digital-humanities/articles/10.3389/fdigh.2018.00005'), \n",
       "    ('DOI', '10.3389/fdigh.2018.00005'), \n",
       "    ('ISSN', '2297-2668'), \n",
       "    ('ABSTRACT', '<p>This paper describes a corpus of about 3,000 English literary texts with about 250 million words extracted from the Gutenberg project that span a range of genres from both fiction and non-fiction written by more than 130 authors (e.g., Darwin, Dickens, Shakespeare). Quantitative narrative analysis (QNA) is used to explore a cleaned subcorpus, the <italic>Gutenberg English Poetry Corpus</italic> (GEPC), which comprises over 100 poetic texts with around two million words from about 50 authors (e.g., Keats, Joyce, Wordsworth). Some exemplary QNA studies show author similarities based on latent semantic analysis, significant topics for each author or various text-analytic metrics for George Eliot’s poem “How Lisa Loved the King” and James Joyce’s “Chamber Music,” concerning, e.g., lexical diversity or sentiment analysis. The GEPC is particularly suited for research in Digital Humanities, Computational Stylistics, or Neurocognitive Poetics, e.g., as training and test corpus for stimulus development and control in empirical studies.</p>')],\n",
       "  persons=OrderedCaseInsensitiveDict([('AUTHOR', [Person('Jacobs, Arthur M.')])]))), ('chakrabarty-etal-2021-mermaid', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{MERMAID}: Metaphor Generation with Symbolism and Discriminative Decoding'), \n",
       "    ('booktitle', 'Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies'), \n",
       "    ('month', 'June'), \n",
       "    ('year', '2021'), \n",
       "    ('address', 'Online'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2021.naacl-main.336'), \n",
       "    ('doi', '10.18653/v1/2021.naacl-main.336'), \n",
       "    ('pages', '4250--4261'), \n",
       "    ('abstract', 'Generating metaphors is a challenging task as it requires a proper understanding of abstract concepts, making connections between unrelated concepts, and deviating from the literal meaning. In this paper, we aim to generate a metaphoric sentence given a literal expression by replacing relevant verbs. Based on a theoretically-grounded connection between metaphors and symbols, we propose a method to automatically construct a parallel corpus by transforming a large number of metaphorical sentences from the Gutenberg Poetry corpus (CITATION) to their literal counterpart using recent advances in masked language modeling coupled with commonsense inference. For the generation task, we incorporate a metaphor discriminator to guide the decoding of a sequence to sequence model fine-tuned on our parallel data to generate high-quality metaphors. Human evaluation on an independent test set of literal statements shows that our best model generates metaphors better than three well-crafted baselines 66{\\\\%} of the time on average. A task-based evaluation shows that human-written poems enhanced with metaphors proposed by our model are preferred 68{\\\\%} of the time compared to poems without metaphors.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Chakrabarty, Tuhin'), Person('Zhang, Xurui'), Person('Muresan, Smaranda'), Person('Peng, Nanyun')]), ('editor', [Person('Toutanova, Kristina'), Person('Rumshisky, Anna'), Person('Zettlemoyer, Luke'), Person('Hakkani-Tur, Dilek'), Person('Beltagy, Iz'), Person('Bethard, Steven'), Person('Cotterell, Ryan'), Person('Chakraborty, Tanmoy'), Person('Zhou, Yichao')])]))), ('chakrabarty-etal-2023-spy', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{I} Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors'), \n",
       "    ('booktitle', 'Findings of the Association for Computational Linguistics: ACL 2023'), \n",
       "    ('month', 'July'), \n",
       "    ('year', '2023'), \n",
       "    ('address', 'Toronto, Canada'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2023.findings-acl.465'), \n",
       "    ('doi', '10.18653/v1/2023.findings-acl.465'), \n",
       "    ('pages', '7370--7388'), \n",
       "    ('abstract', 'Visual metaphors are powerful rhetorical devices used to persuade or communicate creative ideas through images. Similar to linguistic metaphors, they convey meaning implicitly through symbolism and juxtaposition of the symbols. We propose a new task of generating visual metaphors from linguistic metaphors. This is a challenging task for diffusion-based text-to-image models, such as DALL$\\\\cdot$E 2, since it requires the ability to model implicit meaning and compositionality. We propose to solve the task through the collaboration between Large Language Models (LLMs) and Diffusion Models: Instruct GPT-3 (davinci-002) with Chain-of-Thought prompting generates text that represents a visual elaboration of the linguistic metaphor containing the implicit meaning and relevant objects, which is then used as input to the diffusion-based text-to-image models. Using a human-AI collaboration framework, where humans interact both with the LLM and the top-performing diffusion model, we create a high-quality dataset containing 6,476 visual metaphors for 1,540 linguistic metaphors and their associated visual elaborations. Evaluation by professional illustrators shows the promise of LLM-Diffusion Model collaboration for this task.To evaluate the utility of our Human-AI collaboration framework and the quality of our dataset, we perform both an intrinsic human-based evaluation and an extrinsic evaluation using visual entailment as a downstream task.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Chakrabarty, Tuhin'), Person('Saakyan, Arkadiy'), Person('Winn, Olivia'), Person('Panagopoulou, Artemis'), Person('Yang, Yue'), Person('Apidianaki, Marianna'), Person('Muresan, Smaranda')]), ('editor', [Person('Rogers, Anna'), Person('Boyd-Graber, Jordan'), Person('Okazaki, Naoaki')])]))), ('ghazvininejad-etal-2016-generating', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Generating Topical Poetry'), \n",
       "    ('booktitle', 'Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing'), \n",
       "    ('month', 'November'), \n",
       "    ('year', '2016'), \n",
       "    ('address', 'Austin, Texas'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/D16-1126'), \n",
       "    ('doi', '10.18653/v1/D16-1126'), \n",
       "    ('pages', '1183--1191')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Ghazvininejad, Marjan'), Person('Shi, Xing'), Person('Choi, Yejin'), Person('Knight, Kevin')]), ('editor', [Person('Su, Jian'), Person('Duh, Kevin'), Person('Carreras, Xavier')])]))), ('van-de-cruys-2020-automatic', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Automatic Poetry Generation from Prosaic Text'), \n",
       "    ('booktitle', 'Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics'), \n",
       "    ('month', 'July'), \n",
       "    ('year', '2020'), \n",
       "    ('address', 'Online'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2020.acl-main.223'), \n",
       "    ('doi', '10.18653/v1/2020.acl-main.223'), \n",
       "    ('pages', '2471--2480'), \n",
       "    ('abstract', 'In the last few years, a number of successful approaches have emerged that are able to adequately model various aspects of natural language. In particular, language models based on neural networks have improved the state of the art with regard to predictive language modeling, while topic models are successful at capturing clear-cut, semantic dimensions. In this paper, we will explore how these approaches can be adapted and combined to model the linguistic and literary aspects needed for poetry generation. The system is exclusively trained on standard, non-poetic text, and its output is constrained in order to confer a poetic character to the generated verse. The framework is applied to the generation of poems in both English and French, and is equally evaluated for both languages. Even though it only uses standard, non-poetic text as input, the system yields state of the art results for poetry generation.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Van de Cruys, Tim')]), ('editor', [Person('Jurafsky, Dan'), Person('Chai, Joyce'), Person('Schluter, Natalie'), Person('Tetreault, Joel')])]))), ('chakrabarty-etal-2022-help', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Help me write a Poem - Instruction Tuning as a Vehicle for Collaborative Poetry Writing'), \n",
       "    ('booktitle', 'Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing'), \n",
       "    ('month', 'December'), \n",
       "    ('year', '2022'), \n",
       "    ('address', 'Abu Dhabi, United Arab Emirates'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2022.emnlp-main.460'), \n",
       "    ('doi', '10.18653/v1/2022.emnlp-main.460'), \n",
       "    ('pages', '6848--6863'), \n",
       "    ('abstract', \"Recent work in training large language models (LLMs) to follow natural language instructions has opened up exciting opportunities for natural language interface design. Building on the prior success of large language models in the realm of computer assisted creativity, in this work, we present \\\\textit{CoPoet}, a collaborative poetry writing system, with the goal of to study if LLM{'}s actually improve the quality of the generated content. In contrast to auto-completing a user{'}s text, CoPoet is controlled by user instructions that specify the attributes of the desired text, such as \\\\textit{Write a sentence about {`}love{'}} or \\\\textit{Write a sentence ending in {`}fly{'}}. The core component of our system is a language model fine-tuned on a diverse collection of instructions for poetry writing. Our model is not only competitive to publicly available LLMs trained on instructions (InstructGPT), but also capable of satisfying unseen compositional instructions. A study with 15 qualified crowdworkers shows that users successfully write poems with CoPoet on diverse topics ranging from \\\\textit{Monarchy} to \\\\textit{Climate change}, which are preferred by third-party evaluators over poems written without the system.\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Chakrabarty, Tuhin'), Person('Padmakumar, Vishakh'), Person('He, He')]), ('editor', [Person('Goldberg, Yoav'), Person('Kozareva, Zornitsa'), Person('Zhang, Yue')])]))), ('ormazabal-etal-2022-poelm', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{P}oe{LM}: A Meter- and Rhyme-Controllable Language Model for Unsupervised Poetry Generation'), \n",
       "    ('booktitle', 'Findings of the Association for Computational Linguistics: EMNLP 2022'), \n",
       "    ('month', 'December'), \n",
       "    ('year', '2022'), \n",
       "    ('address', 'Abu Dhabi, United Arab Emirates'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2022.findings-emnlp.268'), \n",
       "    ('doi', '10.18653/v1/2022.findings-emnlp.268'), \n",
       "    ('pages', '3655--3670'), \n",
       "    ('abstract', 'Formal verse poetry imposes strict constraints on the meter and rhyme scheme of poems. Most prior work on generating this type of poetry uses existing poems for supervision, which are difficult to obtain for most languages and poetic forms. In this work, we propose an unsupervised approach to generate poems that follow any given meter and rhyme scheme, without requiring any poetic text for training. Our method works by splitting a regular, non-poetic corpus into phrases, prepending control codes that describe the length and end rhyme of each phrase, and training a transformer language model in the augmented corpus. The transformer learns to link the structure descriptor with the control codes to the number of lines, their length and their end rhyme. During inference, we build control codes for the desired meter and rhyme scheme, and condition our language model on them to generate formal verse poetry. Experiments in Spanish and Basque show that our approach is able to generate valid poems, which are often comparable in quality to those written by humans.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Ormazabal, Aitor'), Person('Artetxe, Mikel'), Person('Agirrezabal, Manex'), Person('Soroa, Aitor'), Person('Agirre, Eneko')]), ('editor', [Person('Goldberg, Yoav'), Person('Kozareva, Zornitsa'), Person('Zhang, Yue')])]))), ('tian-peng-2022-zero', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Zero-shot Sonnet Generation with Discourse-level Planning and Aesthetics Features'), \n",
       "    ('booktitle', 'Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies'), \n",
       "    ('month', 'July'), \n",
       "    ('year', '2022'), \n",
       "    ('address', 'Seattle, United States'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2022.naacl-main.262'), \n",
       "    ('doi', '10.18653/v1/2022.naacl-main.262'), \n",
       "    ('pages', '3587--3597'), \n",
       "    ('abstract', 'Poetry generation, and creative language generation in general, usually suffers from the lack of large training data. In this paper, we present a novel framework to generate sonnets that does not require training on poems. We design a hierarchical framework which plans the poem sketch before decoding. Specifically, a content planning module is trained on non-poetic texts to obtain discourse-level coherence; then a rhyme module generates rhyme words and a polishing module introduces imagery and similes for aesthetics purposes. Finally, we design a constrained decoding algorithm to impose the meter-and-rhyme constraint of the generated sonnets. Automatic and human evaluation show that our multi-stage approach without training on poem corpora generates more coherent, poetic, and creative sonnets than several strong baselines.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Tian, Yufei'), Person('Peng, Nanyun')]), ('editor', [Person('Carpuat, Marine'), Person('de Marneffe, Marie-Catherine'), Person('Meza Ruiz, Ivan Vladimir')])]))), ('coil-shwartz-2023-chocolate', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'From chocolate bunny to chocolate crocodile: Do Language Models Understand Noun Compounds?'), \n",
       "    ('booktitle', 'Findings of the Association for Computational Linguistics: ACL 2023'), \n",
       "    ('month', 'July'), \n",
       "    ('year', '2023'), \n",
       "    ('address', 'Toronto, Canada'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2023.findings-acl.169'), \n",
       "    ('doi', '10.18653/v1/2023.findings-acl.169'), \n",
       "    ('pages', '2698--2710'), \n",
       "    ('abstract', \"Noun compound interpretation is the task of expressing a noun compound (e.g. chocolate bunny) in a free-text paraphrase that makes the relationship between the constituent nouns explicit (e.g. bunny-shaped chocolate). We propose modifications to the data and evaluation setup of the standard task (Hendrickx et al., 2013), and show that GPT-3 solves it almost perfectly. We then investigate the task of noun compound conceptualization, i.e. paraphrasing a novel or rare noun compound. E.g., chocolate crocodile is a crocodile-shaped chocolate. This task requires creativity, commonsense, and the ability to generalize knowledge about similar concepts. While GPT-3{'}s performance is not perfect, it is better than that of humans{---}likely thanks to its access to vast amounts of knowledge, and because conceptual processing is effortful for people (Connell and Lynott, 2012). Finally, we estimate the extent to which GPT-3 is reasoning about the world vs. parroting its training data. We find that the outputs from GPT-3 often have significant overlap with a large web corpus, but that the parroting strategy is less beneficial for novel noun compounds.\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Coil, Albert'), Person('Shwartz, Vered')]), ('editor', [Person('Rogers, Anna'), Person('Boyd-Graber, Jordan'), Person('Okazaki, Naoaki')])]))), ('das-ghosh-2017-neuramanteau', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{N}euramanteau: A Neural Network Ensemble Model for Lexical Blends'), \n",
       "    ('booktitle', 'Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)'), \n",
       "    ('month', 'November'), \n",
       "    ('year', '2017'), \n",
       "    ('address', 'Taipei, Taiwan'), \n",
       "    ('publisher', 'Asian Federation of Natural Language Processing'), \n",
       "    ('url', 'https://aclanthology.org/I17-1058'), \n",
       "    ('pages', '576--583'), \n",
       "    ('abstract', 'The problem of blend formation in generative linguistics is interesting in the context of neologism, their quick adoption in modern life and the creative generative process guiding their formation. Blend quality depends on multitude of factors with high degrees of uncertainty. In this work, we investigate if the modern neural network models can sufficiently capture and recognize the creative blend composition process. We propose recurrent neural network sequence-to-sequence models, that are evaluated on multiple blend datasets available in the literature. We propose an ensemble neural and hybrid model that outperforms most of the baselines and heuristic models upon evaluation on test data.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Das, Kollol'), Person('Ghosh, Shaona')]), ('editor', [Person('Kondrak, Greg'), Person('Watanabe, Taro')])]))), ('Dathathri2019PlugAP', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Plug and Play Language Models: A Simple Approach to Controlled Text Generation'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2019'), \n",
       "    ('volume', 'abs/1912.02164'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:208617790')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Dathathri, Sumanth'), Person('Madotto, Andrea'), Person('Lan, Janice'), Person('Hung, Jane'), Person('Frank, Eric'), Person('Molino, Piero'), Person('Yosinski, Jason'), Person('Liu, Rosanne')])]))), ('Pascual2021APM', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'A Plug-and-Play Method for Controlled Text Generation'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2021'), \n",
       "    ('volume', 'abs/2109.09707'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:237571784')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Pascual, Damian'), Person(\"Egressy, B{\\\\'e}ni\"), Person('Meister, Clara'), Person('Cotterell, Ryan'), Person('Wattenhofer, Roger')])]))), ('Tambwekar2018ControllableNS', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Controllable Neural Story Plot Generation via Reward Shaping'), \n",
       "    ('booktitle', 'International Joint Conference on Artificial Intelligence'), \n",
       "    ('year', '2018'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:199465680')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Tambwekar, Pradyumna'), Person('Dhuliawala, Murtaza'), Person('Martin, Lara J.'), Person('Mehta, Animesh'), Person('Harrison, Brent'), Person('Riedl, Mark O.')])]))), ('Chung2022TaleBrushSS', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'TaleBrush: Sketching Stories with Generative Pretrained Language Models'), \n",
       "    ('journal', 'Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems'), \n",
       "    ('year', '2022'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:247625751')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Chung, John Joon Young'), Person('Kim, Wooseok'), Person('Yoo, Kang Min'), Person('Lee, Hwaran'), Person('Adar, Eytan'), Person('Chang, Minsuk')])]))), ('Rashkin2020PlotMachinesOG', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2020'), \n",
       "    ('volume', 'abs/2004.14967'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:216868683')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Rashkin, Hannah'), Person('Celikyilmaz, Asli'), Person('Choi, Yejin'), Person('Gao, Jianfeng')])]))), ('sun-etal-2023-evaluating', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Evaluating Large Language Models on Controlled Generation Tasks'), \n",
       "    ('booktitle', 'Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing'), \n",
       "    ('month', 'December'), \n",
       "    ('year', '2023'), \n",
       "    ('address', 'Singapore'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2023.emnlp-main.190'), \n",
       "    ('doi', '10.18653/v1/2023.emnlp-main.190'), \n",
       "    ('pages', '3155--3168'), \n",
       "    ('abstract', 'While recent studies have looked into the abilities of large language models in various benchmark tasks, including question generation, reading comprehension, multilingual and etc, there have been few studies looking into the controllability of large language models on generation tasks. We present an extensive analysis of various benchmarks including a sentence planning benchmark with different granularities. After comparing large language models against state-of-the-start finetuned smaller models, we present a spectrum showing large language models falling behind, are comparable, or exceed the ability of smaller models. We conclude that *large language models struggle at meeting fine-grained hard constraints*.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Sun, Jiao'), Person('Tian, Yufei'), Person('Zhou, Wangchunshu'), Person('Xu, Nan'), Person('Hu, Qian'), Person('Gupta, Rahul'), Person('Wieting, John'), Person('Peng, Nanyun'), Person('Ma, Xuezhe')]), ('editor', [Person('Bouamor, Houda'), Person('Pino, Juan'), Person('Bali, Kalika')])]))), ('Elazar2021MeasuringAI', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Measuring and Improving Consistency in Pretrained Language Models'), \n",
       "    ('journal', 'Transactions of the Association for Computational Linguistics'), \n",
       "    ('year', '2021'), \n",
       "    ('volume', '9'), \n",
       "    ('pages', '1012-1031'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:231740560')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Elazar, Yanai'), Person('Kassner, Nora'), Person('Ravfogel, Shauli'), Person('Ravichander, Abhilasha'), Person('Hovy, Eduard H.'), Person('Sch{\\\\\"u}tze, Hinrich'), Person('Goldberg, Yoav')])]))), ('Zhang2023HowLM', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'How Language Model Hallucinations Can Snowball'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2023'), \n",
       "    ('volume', 'abs/2305.13534'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:258841857')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Zhang, Muru'), Person('Press, Ofir'), Person('Merrill, William'), Person('Liu, Alisa'), Person('Smith, Noah A.')])]))), ('Tam2022EvaluatingTF', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Evaluating the Factual Consistency of Large Language Models Through News Summarization'), \n",
       "    ('booktitle', 'Annual Meeting of the Association for Computational Linguistics'), \n",
       "    ('year', '2022'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:253523092')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Tam, Derek'), Person('Mascarenhas, Anisha'), Person('Zhang, Shiyue'), Person('Kwan, Sarah'), Person('Bansal, Mohit'), Person('Raffel, Colin')])]))), ('Anderson2024HomogenizationEO', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Homogenization Effects of Large Language Models on Human Creative Ideation'), \n",
       "    ('journal', 'Proceedings of the 16th Conference on Creativity \\\\& Cognition'), \n",
       "    ('year', '2024'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:267406608')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Anderson, Barrett R'), Person('Shah, Jash Hemant'), Person('Kreminski, Max')])]))), ('Mirowski2022CoWritingSA', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Co-Writing Screenplays and Theatre Scripts with Language Models: Evaluation by Industry Professionals'), \n",
       "    ('journal', 'Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems'), \n",
       "    ('year', '2022'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:252596159')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Mirowski, Piotr Wojciech'), Person('Mathewson, Kory Wallace'), Person('Pittman, Jaylen'), Person('Evans, Richard')])]))), ('Schick2022PEERAC', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'PEER: A Collaborative Language Model'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2022'), \n",
       "    ('volume', 'abs/2208.11663'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:251765117')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Schick, Timo'), Person('Dwivedi-Yu, Jane'), Person('Jiang, Zhengbao'), Person('Petroni, Fabio'), Person('Lewis, Patrick'), Person('Izacard, Gautier'), Person('You, Qingfei'), Person('Nalmpantis, Christoforos'), Person('Grave, Edouard'), Person('Riedel, Sebastian')])]))), ('Chakrabarty2023CreativitySI', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Creativity Support in the Age of Large Language Models: An Empirical Study Involving Emerging Writers'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2023'), \n",
       "    ('volume', 'abs/2309.12570'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:262217523')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Chakrabarty, Tuhin'), Person('Padmakumar, Vishakh'), Person('Brahman, Faeze'), Person('Muresan, Smaranda')])]))), ('Guan2020UNIONAU', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'UNION: An Unreferenced Metric for Evaluating Open-ended Story Generation'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2020'), \n",
       "    ('volume', 'abs/2009.07602'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:221739231')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Guan, Jian'), Person('Huang, Minlie')])]))), ('Chen2022StoryERAS', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'StoryER: Automatic Story Evaluation via Ranking, Rating and Reasoning'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2022'), \n",
       "    ('volume', 'abs/2210.08459'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:252918409')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Chen, Hong'), Person('Vo, Duc Minh'), Person('Takamura, Hiroya'), Person('Miyao, Yusuke'), Person('Nakayama, Hideki')])]))), ('Jiang2023BRAINTEASERLT', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'BRAINTEASER: Lateral Thinking Puzzles for Large Language Models'), \n",
       "    ('booktitle', 'Conference on Empirical Methods in Natural Language Processing'), \n",
       "    ('year', '2023'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:263830212')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Jiang, Yifan'), Person('Ilievski, Filip'), Person('Ma, Kaixin')])]))), ('Lin2021RiddleSenseRA', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge'), \n",
       "    ('booktitle', 'Findings'), \n",
       "    ('year', '2021'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:235731975')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Lin, Bill Yuchen'), Person('Wu, Ziyi'), Person('Yang, Yichi'), Person('Lee, Dong-Ho'), Person('Ren, Xiang')])]))), ('Zhao2023UNcommonsenseRA', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2023'), \n",
       "    ('volume', 'abs/2311.08469'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:265213087')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Zhao, Wenting'), Person('Chiu, Justin T'), Person('Hwang, Jena D.'), Person('Brahman, Faeze'), Person('Hessel, Jack'), Person('Choudhury, Sanjiban'), Person('Choi, Yejin'), Person('Li, Xiang Lorraine'), Person('Suhr, Alane')])]))), ('samadarshi2024connectingdotsevaluatingabstract', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Connecting the Dots: Evaluating Abstract Reasoning Capabilities of LLMs Using the New York Times Connections Word Game'), \n",
       "    ('year', '2024'), \n",
       "    ('eprint', '2406.11012'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL'), \n",
       "    ('url', 'https://arxiv.org/abs/2406.11012')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Samadarshi, Prisha'), Person('Mustafa, Mariam'), Person('Kulkarni, Anushka'), Person('Rothkopf, Raven'), Person('Chakrabarty, Tuhin'), Person('Muresan, Smaranda')])]))), ('agarwal2020acrosticpoemgeneration', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Acrostic Poem Generation'), \n",
       "    ('year', '2020'), \n",
       "    ('eprint', '2010.02239'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL'), \n",
       "    ('url', 'https://arxiv.org/abs/2010.02239')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Agarwal, Rajat'), Person('Kann, Katharina')])]))), ('Liu2018beyond', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('series', 'MM ’18'), \n",
       "    ('title', 'Beyond Narrative Description: Generating Poetry from Images by Multi-Adversarial Training'), \n",
       "    ('url', 'http://dx.doi.org/10.1145/3240508.3240587'), \n",
       "    ('DOI', '10.1145/3240508.3240587'), \n",
       "    ('booktitle', 'Proceedings of the 26th ACM international conference on Multimedia'), \n",
       "    ('publisher', 'ACM'), \n",
       "    ('year', '2018'), \n",
       "    ('month', 'October'), \n",
       "    ('collection', 'MM ’18')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Liu, Bei'), Person('Fu, Jianlong'), Person('Kato, Makoto P.'), Person('Yoshikawa, Masatoshi')])]))), ('du-chilton-2023-storywars', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{S}tory{W}ars: A Dataset and Instruction Tuning Baselines for Collaborative Story Understanding and Generation'), \n",
       "    ('booktitle', 'Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)'), \n",
       "    ('month', 'July'), \n",
       "    ('year', '2023'), \n",
       "    ('address', 'Toronto, Canada'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2023.acl-long.171'), \n",
       "    ('doi', '10.18653/v1/2023.acl-long.171'), \n",
       "    ('pages', '3044--3062'), \n",
       "    ('abstract', \"Collaborative stories, which are texts created through the collaborative efforts of multiple authors with different writing styles and intentions, pose unique challenges for NLP models. Understanding and generating such stories remains an underexplored area due to the lack of open-domain corpora. To address this, we introduce StoryWars, a new dataset of over 40,000 collaborative stories written by 9,400 different authors from an online platform. We design 12 task types, comprising 7 understanding and 5 generation task types, on {pasted macro {`}STORYWARS{'}}, deriving 101 diverse story-related tasks in total as a multi-task benchmark covering all fully-supervised, few-shot, and zero-shot scenarios. Furthermore, we present our instruction-tuned model, InstructStory, for the story tasks showing that instruction tuning, in addition to achieving superior results in zero-shot and few-shot scenarios, can also obtain the best performance on the fully-supervised tasks in StoryWars, establishing strong multi-task benchmark performances on StoryWars.\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Du, Yulun'), Person('Chilton, Lydia')]), ('editor', [Person('Rogers, Anna'), Person('Boyd-Graber, Jordan'), Person('Okazaki, Naoaki')])]))), ('louis-sutton-2018-deep', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Deep Dungeons and Dragons: Learning Character-Action Interactions from Role-Playing Game Transcripts'), \n",
       "    ('booktitle', 'Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)'), \n",
       "    ('month', 'June'), \n",
       "    ('year', '2018'), \n",
       "    ('address', 'New Orleans, Louisiana'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/N18-2111'), \n",
       "    ('doi', '10.18653/v1/N18-2111'), \n",
       "    ('pages', '708--713'), \n",
       "    ('abstract', 'An essential aspect to understanding narratives is to grasp the interaction between characters in a story and the actions they take. We examine whether computational models can capture this interaction, when both character attributes and actions are expressed as complex natural language descriptions. We propose role-playing games as a testbed for this problem, and introduce a large corpus of game transcripts collected from online discussion forums. Using neural language models which combine character and action descriptions from these stories, we show that we can learn the latent ties. Action sequences are better predicted when the character performing the action is also taken into account, and vice versa for character attributes.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Louis, Annie'), Person('Sutton, Charles')]), ('editor', [Person('Walker, Marilyn'), Person('Ji, Heng'), Person('Stent, Amanda')])]))), ('hong-etal-2023-visual-writing', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences'), \n",
       "    ('journal', 'Transactions of the Association for Computational Linguistics'), \n",
       "    ('volume', '11'), \n",
       "    ('year', '2023'), \n",
       "    ('address', 'Cambridge, MA'), \n",
       "    ('publisher', 'MIT Press'), \n",
       "    ('url', 'https://aclanthology.org/2023.tacl-1.33'), \n",
       "    ('doi', '10.1162/tacl_a_00553'), \n",
       "    ('pages', '565--581'), \n",
       "    ('abstract', 'Current work on image-based story generation suffers from the fact that the existing image sequence collections do not have coherent plots behind them. We improve visual story generation by producing a new image-grounded dataset, Visual Writing Prompts (VWP). VWP contains almost 2K selected sequences of movie shots, each including 5-10 images. The image sequences are aligned with a total of 12K stories which were collected via crowdsourcing given the image sequences and a set of grounded characters from the corresponding image sequence. Our new image sequence collection and filtering process has allowed us to obtain stories that are more coherent, diverse, and visually grounded compared to previous work. We also propose a character-based story generation model driven by coherence as a strong baseline. Evaluations show that our generated stories are more coherent, visually grounded, and diverse than stories generated with the current state-of-the-art model. Our code, image features, annotations and collected stories are available at \\\\url{https://vwprompt.github.io/}.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Hong, Xudong'), Person('Sayeed, Asad'), Person('Mehra, Khushboo'), Person('Demberg, Vera'), Person('Schiele, Bernt')])]))), ('hossain-etal-2019-president', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', \"{``}President Vows to Cut {\\\\textless}Taxes{\\\\textgreater} Hair{''}: Dataset and Analysis of Creative Text Editing for Humorous Headlines\"), \n",
       "    ('booktitle', 'Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)'), \n",
       "    ('month', 'June'), \n",
       "    ('year', '2019'), \n",
       "    ('address', 'Minneapolis, Minnesota'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/N19-1012'), \n",
       "    ('doi', '10.18653/v1/N19-1012'), \n",
       "    ('pages', '133--142'), \n",
       "    ('abstract', 'We introduce, release, and analyze a new dataset, called Humicroedit, for research in computational humor. Our publicly available data consists of regular English news headlines paired with versions of the same headlines that contain simple replacement edits designed to make them funny. We carefully curated crowdsourced editors to create funny headlines and judges to score a to a total of 15,095 edited headlines, with five judges per headline. The simple edits, usually just a single word replacement, mean we can apply straightforward analysis techniques to determine what makes our edited headlines humorous. We show how the data support classic theories of humor, such as incongruity, superiority, and setup/punchline. Finally, we develop baseline classifiers that can predict whether or not an edited headline is funny, which is a first step toward automatically generating humorous headlines as an approach to creating topical humor.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Hossain, Nabil'), Person('Krumm, John'), Person('Gamon, Michael')]), ('editor', [Person('Burstein, Jill'), Person('Doran, Christy'), Person('Solorio, Thamar')])]))), ('kayatani2021', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('year', '2021'), \n",
       "    ('month', '01'), \n",
       "    ('pages', '2072-2081'), \n",
       "    ('title', 'The Laughing Machine: Predicting Humor in Video'), \n",
       "    ('doi', '10.1109/WACV48630.2021.00212')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Kayatani, Yuta'), Person('Yang, Zekun'), Person('Otani, Mayu'), Person('Garcia, Noa'), Person('Chu, Chenhui'), Person('Nakashima, Yuta'), Person('Takemura, Haruo')])]))), ('stock-strapparava-2005-hahacronym', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{HAHA}cronym: A Computational Humor System'), \n",
       "    ('booktitle', 'Proceedings of the {ACL} Interactive Poster and Demonstration Sessions'), \n",
       "    ('month', 'June'), \n",
       "    ('year', '2005'), \n",
       "    ('address', 'Ann Arbor, Michigan'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/P05-3029'), \n",
       "    ('doi', '10.3115/1225753.1225782'), \n",
       "    ('pages', '113--116')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Stock, Oliviero'), Person('Strapparava, Carlo')]), ('editor', [Person('Nagata, Masaaki'), Person('Pedersen, Ted')])]))), ('meaney-etal-2021-semeval', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{S}em{E}val 2021 Task 7: {H}a{H}ackathon, Detecting and Rating Humor and Offense'), \n",
       "    ('booktitle', 'Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)'), \n",
       "    ('month', 'August'), \n",
       "    ('year', '2021'), \n",
       "    ('address', 'Online'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2021.semeval-1.9'), \n",
       "    ('doi', '10.18653/v1/2021.semeval-1.9'), \n",
       "    ('pages', '105--119'), \n",
       "    ('abstract', 'SemEval 2021 Task 7, HaHackathon, was the first shared task to combine the previously separate domains of humor detection and offense detection. We collected 10,000 texts from Twitter and the Kaggle Short Jokes dataset, and had each annotated for humor and offense by 20 annotators aged 18-70. Our subtasks were binary humor detection, prediction of humor and offense ratings, and a novel controversy task: to predict if the variance in the humor ratings was higher than a specific threshold. The subtasks attracted 36-58 submissions, with most of the participants choosing to use pre-trained language models. Many of the highest performing teams also implemented additional optimization techniques, including task-adaptive training and adversarial training. The results suggest that the participating systems are well suited to humor detection, but that humor controversy is a more challenging task. We discuss which models excel in this task, which auxiliary techniques boost their performance, and analyze the errors which were not captured by the best systems.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Meaney, J. A.'), Person('Wilson, Steven'), Person('Chiruzzo, Luis'), Person('Lopez, Adam'), Person('Magdy, Walid')]), ('editor', [Person('Palmer, Alexis'), Person('Schneider, Nathan'), Person('Schluter, Natalie'), Person('Emerson, Guy'), Person('Herbelot, Aurelie'), Person('Zhu, Xiaodan')])]))), ('miller-etal-2017-semeval', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{S}em{E}val-2017 Task 7: Detection and Interpretation of {E}nglish Puns'), \n",
       "    ('booktitle', 'Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)'), \n",
       "    ('month', 'August'), \n",
       "    ('year', '2017'), \n",
       "    ('address', 'Vancouver, Canada'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/S17-2005'), \n",
       "    ('doi', '10.18653/v1/S17-2005'), \n",
       "    ('pages', '58--68'), \n",
       "    ('abstract', \"A pun is a form of wordplay in which a word suggests two or more meanings by exploiting polysemy, homonymy, or phonological similarity to another word, for an intended humorous or rhetorical effect. Though a recurrent and expected feature in many discourse types, puns stymie traditional approaches to computational lexical semantics because they violate their one-sense-per-context assumption. This paper describes the first competitive evaluation for the automatic detection, location, and interpretation of puns. We describe the motivation for these tasks, the evaluation methods, and the manually annotated data set. Finally, we present an overview and discussion of the participating systems{'} methodologies, resources, and results.\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Miller, Tristan'), Person('Hempelmann, Christian'), Person('Gurevych, Iryna')]), ('editor', [Person('Bethard, Steven'), Person('Carpuat, Marine'), Person('Apidianaki, Marianna'), Person('Mohammad, Saif M.'), Person('Cer, Daniel'), Person('Jurgens, David')])]))), ('Mitchell2023ComparingHG', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Comparing Humans, GPT-4, and GPT-4V On Abstraction and Reasoning Tasks'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2023'), \n",
       "    ('volume', 'abs/2311.09247'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:265220802')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Mitchell, Melanie'), Person('Palmarini, Alessandro B.'), Person('Moskvichev, Arseny')])]))), ('Chollet2019OnTM', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'On the Measure of Intelligence'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2019'), \n",
       "    ('volume', 'abs/1911.01547'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:207870692')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Chollet, François')])]))), ('Moskvichev2023TheCB', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'The ConceptARC Benchmark: Evaluating Understanding and Generalization in the ARC Domain'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2023'), \n",
       "    ('volume', 'abs/2305.07141'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:258676355')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Moskvichev, Arseny'), Person('Odouard, Victor Vikram'), Person('Mitchell, Melanie')])]))), ('Xu2023LLMsAT', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2023'), \n",
       "    ('volume', 'abs/2305.18354'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:258968016')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Xu, Yudong'), Person('Li, Wenhao'), Person('Vaezipoor, Pashootan'), Person('Sanner, Scott'), Person('Khalil, Elias Boutros')])]))), ('Xu2022GraphsCA', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Graphs, Constraints, and Search for the Abstraction and Reasoning Corpus'), \n",
       "    ('booktitle', 'AAAI Conference on Artificial Intelligence'), \n",
       "    ('year', '2022'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:252968055')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Xu, Yudong'), Person('Khalil, Elias Boutros'), Person('Sanner, Scott')])]))), ('Odouard2022EvaluatingUO', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Evaluating Understanding on Conceptual Abstraction Benchmarks'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2022'), \n",
       "    ('volume', 'abs/2206.14187'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:250089291')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Odouard, Victor Vikram'), Person('Mitchell, Melanie')])]))), ('Mirchandani2023LargeLM', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Large Language Models as General Pattern Machines'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2023'), \n",
       "    ('volume', 'abs/2307.04721'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:259501163')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Mirchandani, Suvir'), Person('Xia, F.'), Person('Florence, Peter R.'), Person('Ichter, Brian'), Person('Driess, Danny'), Person('Arenas, Montse Gonzalez'), Person('Rao, Kanishka'), Person('Sadigh, Dorsa'), Person('Zeng, Andy')])]))), ('John2003RavenPM', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Raven Progressive Matrices'), \n",
       "    ('year', '1938'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:61756432')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Raven, John Carlyle')])]))), ('Ahrabian2024TheCC', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'The Curious Case of Nonverbal Abstract Reasoning with Multi-Modal Large Language Models'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2024'), \n",
       "    ('volume', 'abs/2401.12117'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:267068363')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Ahrabian, Kian'), Person('Sourati, Zhivar'), Person('Sun, Kexuan'), Person('Zhang, Jiarui'), Person('Jiang, Yifan'), Person('Morstatter, Fred'), Person('Pujara, Jay')])]))), ('Gendron2023LargeLM', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Large Language Models Are Not Strong Abstract Reasoners'), \n",
       "    ('year', '2023'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:258988045')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Gendron, Ga{\\\\\"e}l'), Person('Bao, Qiming'), Person('Witbrock, M.'), Person('Dobbie, Gillian')])]))), ('Sourati2023ARNAR', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'ARN: Analogical Reasoning on Narratives'), \n",
       "    ('year', '2023'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:263605669')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Sourati, Zhivar'), Person('Ilievski, Filip'), Person('Sommerauer, Pia')])]))), ('Mitchell_2021', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Abstraction and analogy‐making in artificial intelligence'), \n",
       "    ('volume', '1505'), \n",
       "    ('ISSN', '1749-6632'), \n",
       "    ('url', 'http://dx.doi.org/10.1111/nyas.14619'), \n",
       "    ('DOI', '10.1111/nyas.14619'), \n",
       "    ('number', '1'), \n",
       "    ('journal', 'Annals of the New York Academy of Sciences'), \n",
       "    ('publisher', 'Wiley'), \n",
       "    ('year', '2021'), \n",
       "    ('month', 'June'), \n",
       "    ('pages', '79–101')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Mitchell, Melanie')])]))), ('hofstadter2001', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Analogy as the Core of Cognition'), \n",
       "    ('journal', 'MIT Press'), \n",
       "    ('year', '2001')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Hofstadter, Douglas')])]))), ('Bongard1970PatternR', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Pattern recognition'), \n",
       "    ('year', '1970'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:267827262')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Bongard, M. M.')])]))), ('Raven2016RavenSP', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Raven Standard Progressive Matrices'), \n",
       "    ('year', '2016'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:149749549')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Raven, John Carlyle')])]))), ('Santoro2018MeasuringAR', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Measuring abstract reasoning in neural networks'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2018'), \n",
       "    ('volume', 'abs/1807.04225'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:49665167')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Santoro, Adam'), Person('Hill, Felix'), Person('Barrett, David G. T.'), Person('Morcos, Ari S.'), Person('Lillicrap, Timothy P.')])]))), ('Zhang2019RAVENAD', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'RAVEN: A Dataset for Relational and Analogical Visual REasoNing'), \n",
       "    ('journal', '2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)'), \n",
       "    ('year', '2019'), \n",
       "    ('pages', '5312-5322'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:71148268')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Zhang, Chi'), Person('Gao, Feng'), Person('Jia, Baoxiong'), Person('Zhu, Yixin'), Person('Zhu, Song-Chun')])]))), ('Kumar2024ComparingAI', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2024'), \n",
       "    ('volume', 'abs/2402.03618'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:267499989')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Kumar, Sreejan'), Person('Marjieh, Raja'), Person('Zhang, Byron'), Person('Campbell, Declan'), Person('Hu, Michael Y.'), Person('Bhatt, Umang'), Person('Lake, Brenden'), Person('Griffiths, Thomas L.')])]))), ('Holzinger2019KANDINSKYPA', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'KANDINSKY Patterns as IQ-Test for Machine Learning'), \n",
       "    ('booktitle', 'International Cross-Domain Conference on Machine Learning and Knowledge Extraction'), \n",
       "    ('year', '2019'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:201616604')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Holzinger, Andreas'), Person('Kickmeier-Rust, Michael D.'), Person('M{\\\\\"u}ller, Heimo')])]))), ('Lorello2024TheKB', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'The KANDY Benchmark: Incremental Neuro-Symbolic Learning and Reasoning with Kandinsky Patterns'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2024'), \n",
       "    ('volume', 'abs/2402.17431'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:268033655')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Lorello, Luca Salvatore'), Person('Lippi, Marco'), Person('Melacci, Stefano')])]))), ('Zhang2021ACREAC', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'ACRE: Abstract Causal REasoning Beyond Covariation'), \n",
       "    ('journal', '2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)'), \n",
       "    ('year', '2021'), \n",
       "    ('pages', '10638-10648'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:232380074')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Zhang, Chi'), Person('Jia, Baoxiong'), Person('Edmonds, Mark'), Person('Zhu, Song-Chun'), Person('Zhu, Yixin')])]))), ('Langlois2021SerialRR', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Serial reproduction reveals the geometry of visuospatial representations'), \n",
       "    ('journal', 'Proceedings of the National Academy of Sciences of the United States of America'), \n",
       "    ('year', '2021'), \n",
       "    ('volume', '118'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:232376763')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Langlois, Thomas A.'), Person('Jacoby, Nori'), Person('Suchow, Jordan W.'), Person('Griffiths, Thomas L.')])]))), ('Jacob2023FAMEFS', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'FAME: Flexible, Scalable Analogy Mappings Engine'), \n",
       "    ('booktitle', 'Conference on Empirical Methods in Natural Language Processing'), \n",
       "    ('year', '2023'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:259267034')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Jacob, Shahar'), Person('Shani, Chen'), Person('Shahaf, Dafna')])]))), ('Gentner1983StructureMappingAT', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Structure-Mapping: A Theoretical Framework for Analogy'), \n",
       "    ('journal', 'Cogn. Sci.'), \n",
       "    ('year', '1983'), \n",
       "    ('volume', '7'), \n",
       "    ('pages', '155-170'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:5371492')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Gentner, Dedre')])]))), ('Falkenhainer1989TheSE', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'The Structure-Mapping Engine: Algorithm and Examples'), \n",
       "    ('journal', 'Artif. Intell.'), \n",
       "    ('year', '1989'), \n",
       "    ('volume', '41'), \n",
       "    ('pages', '1-63'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:8751960')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Falkenhainer, Brian'), Person('Forbus, Kenneth D.'), Person('Gentner, Dedre')])]))), ('Turney2008TheLR', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'The Latent Relation Mapping Engine: Algorithm and Experiments'), \n",
       "    ('journal', 'J. Artif. Intell. Res.'), \n",
       "    ('year', '2008'), \n",
       "    ('volume', '33'), \n",
       "    ('pages', '615-655'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:7112602')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Turney, Peter D.')])]))), ('kaufman2009fourc', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Beyond Big and Little: The Four C Model of Creativity'), \n",
       "    ('journal', 'Review of General Psychology'), \n",
       "    ('volume', '13'), \n",
       "    ('number', '1'), \n",
       "    ('pages', '1-12'), \n",
       "    ('year', '2009'), \n",
       "    ('doi', '10.1037/a0013688'), \n",
       "    ('URL', 'https://doi.org/10.1037/a0013688'), \n",
       "    ('eprint', 'https://doi.org/10.1037/a0013688'), \n",
       "    ('abstract', 'Most investigations of creativity tend to take one of two directions: everyday creativity (also called “little-c”), which can be found in nearly all people, and eminent creativity (also called “Big-C”), which is reserved for the great. In this paper, the authors propose a Four C model of creativity that expands this dichotomy. Specifically, the authors add the idea of “mini-c,” creativity inherent in the learning process, and Pro-c, the developmental and effortful progression beyond little-c that represents professional-level expertise in any creative area. The authors include different transitions and gradations of these four dimensions of creativity, and then discuss advantages and examples of the Four C Model.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Kaufman, James C.'), Person('Beghetto, Ronald A.')])]))), ('wallach1965modes', Entry('book',\n",
       "  fields=[\n",
       "    ('title', 'Modes of Thinking in Young Children: A Study of the Creativity-intelligence Distinction'), \n",
       "    ('isbn', '9780030525957'), \n",
       "    ('lccn', '65021085'), \n",
       "    ('series', 'Modes of Thinking in Young Children: A Study of the Creativity-intelligence Distinction'), \n",
       "    ('url', 'https://books.google.ch/books?id=rAoNAAAAIAAJ'), \n",
       "    ('year', '1965'), \n",
       "    ('publisher', 'Holt, Rinehart and Winston')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Wallach, M.A.'), Person('Kogan, N.')])]))), ('pease2002', Entry('article',\n",
       "  fields=[\n",
       "    ('year', '2002'), \n",
       "    ('month', '10'), \n",
       "    ('pages', ''), \n",
       "    ('title', 'Evaluating Machine Creativity')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Pease, Alison'), Person('Winterstein, Daniel'), Person('Colton, Simon')])]))), ('Ritchie2007SomeEC', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Some Empirical Criteria for Attributing Creativity to a Computer Program'), \n",
       "    ('journal', 'Minds and Machines'), \n",
       "    ('year', '2007'), \n",
       "    ('volume', '17'), \n",
       "    ('pages', '67-99'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:7947377')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Ritchie, Graeme D.')])]))), ('nath2024characterisingcreativeprocesshumans', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Characterising the Creative Process in Humans and Large Language Models'), \n",
       "    ('year', '2024'), \n",
       "    ('eprint', '2405.00899'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.HC'), \n",
       "    ('url', 'https://arxiv.org/abs/2405.00899')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Nath, Surabhi S.'), Person('Dayan, Peter'), Person('Stevenson, Claire')])]))), ('baas2013', Entry('article',\n",
       "  fields=[\n",
       "    ('year', '2013'), \n",
       "    ('month', '10'), \n",
       "    ('pages', ''), \n",
       "    ('title', 'Personality and Creativity: The Dual Pathway to Creativity Model and a Research Agenda'), \n",
       "    ('volume', '7'), \n",
       "    ('journal', 'Social and Personality Psychology Compass'), \n",
       "    ('doi', '10.1111/spc3.12062')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Baas, Matthijs'), Person('Roskes, Marieke'), Person('Sligte, Daniel'), Person('Nijstad, Bernard'), Person('De Dreu, Carsten')])]))), ('Nijstad2010TheDP', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'The dual pathway to creativity model: Creative ideation as a function of flexibility and persistence'), \n",
       "    ('journal', 'European Review of Social Psychology'), \n",
       "    ('year', '2010'), \n",
       "    ('volume', '21'), \n",
       "    ('pages', '34 - 77'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:145290967')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Nijstad, Bernard A.'), Person('Dreu, C. D. De'), Person('Rietzschel, Eric F.'), Person('Baas, Matthijs')])]))), ('Yuan2022WordcraftSW', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Wordcraft: Story Writing With Large Language Models'), \n",
       "    ('journal', 'Proceedings of the 27th International Conference on Intelligent User Interfaces'), \n",
       "    ('year', '2022'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:247585187')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Yuan, Ann'), Person('Coenen, Andy'), Person('Reif, Emily'), Person('Ippolito, Daphne')])]))), ('Qiao2022ReasoningWL', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Reasoning with Language Model Prompting: A Survey'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2022'), \n",
       "    ('volume', 'abs/2212.09597'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:254854219')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Qiao, Shuofei'), Person('Ou, Yixin'), Person('Zhang, Ningyu'), Person('Chen, Xiang'), Person('Yao, Yunzhi'), Person('Deng, Shumin'), Person('Tan, Chuanqi'), Person('Huang, Fei'), Person('Chen, Huajun')])]))), ('meister-etal-2022-high', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'On the probability{--}quality paradox in language generation'), \n",
       "    ('booktitle', 'Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)'), \n",
       "    ('month', 'May'), \n",
       "    ('year', '2022'), \n",
       "    ('address', 'Dublin, Ireland'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2022.acl-short.5'), \n",
       "    ('doi', '10.18653/v1/2022.acl-short.5'), \n",
       "    ('pages', '36--45'), \n",
       "    ('abstract', 'When generating natural language from neural probabilistic models, high probability does not always coincide with high quality: It has often been observed that mode-seeking decoding methods, i.e., those that produce high-probability text under the model, lead to unnatural language. On the other hand, the lower-probability text generated by stochastic methods is perceived as more human-like. In this note, we offer an explanation for this phenomenon by analyzing language generation through an information-theoretic lens. Specifically, we posit that human-like language should contain an amount of information (quantified as negative log-probability) that is close to the entropy of the distribution over natural strings. Further, we posit that language with substantially more (or less) information is undesirable. We provide preliminary empirical evidence in favor of this hypothesis; quality ratings of both human and machine-generated text{---}covering multiple tasks and common decoding strategies{---}suggest high-quality text has an information content significantly closer to the entropy than we would expect by chance.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Meister, Clara'), Person('Wiher, Gian'), Person('Pimentel, Tiago'), Person('Cotterell, Ryan')]), ('editor', [Person('Muresan, Smaranda'), Person('Nakov, Preslav'), Person('Villavicencio, Aline')])]))), ('meister-etal-2023-locally', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Locally Typical Sampling'), \n",
       "    ('journal', 'Transactions of the Association for Computational Linguistics'), \n",
       "    ('volume', '11'), \n",
       "    ('year', '2023'), \n",
       "    ('address', 'Cambridge, MA'), \n",
       "    ('publisher', 'MIT Press'), \n",
       "    ('url', 'https://aclanthology.org/2023.tacl-1.7'), \n",
       "    ('doi', '10.1162/tacl_a_00536'), \n",
       "    ('pages', '102--121'), \n",
       "    ('abstract', \"Today{'}s probabilistic language generators fall short when it comes to producing coherent and fluent text despite the fact that the underlying models perform well under standard metrics (e.g., perplexity). This discrepancy has puzzled the language generation community for the last few years. In this work, we posit that the abstraction of natural language generation as a discrete stochastic process{---}which allows for an information-theoretic analysis{---}can provide new insights into the behavior of probabilistic language generators, for example, why high-probability texts can be dull or repetitive. Humans use language as a means of communicating information, aiming to do so in a simultaneously efficient and error-minimizing manner; in fact, psycholinguistics research suggests humans choose each word in a string with this subconscious goal in mind. We formally define the set of strings that meet this criterion: Those for which each word has an information content close to the expected information content, namely, the conditional entropy of our model. We then propose a simple and efficient procedure for enforcing this criterion when generating from probabilistic models, which we call locally typical sampling. Automatic and human evaluations show that, in comparison to nucleus and top-k sampling, locally typical sampling offers competitive performance (in both abstractive summarization and story generation) in terms of quality while consistently reducing degenerate repetitions.\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Meister, Clara'), Person('Pimentel, Tiago'), Person('Wiher, Gian'), Person('Cotterell, Ryan')])]))), ('Raskin1994NonliteralnessAN', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Non-literalness and non-bona-f{\\\\^i}de in language: An approach to formal and computational treatments of humor'), \n",
       "    ('journal', 'Pragmatics \\\\& Cognition'), \n",
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       "    ('title', 'DALL-E 2 Fails to Reliably Capture Common Syntactic Processes'), \n",
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       "    ('journal', 'ArXiv'), \n",
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       "    ('volume', 'abs/2304.06470'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:257826680')],\n",
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       "    ('title', 'DALLE-2 is Seeing Double: Flaws in Word-to-Concept Mapping in Text2Image Models'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2022'), \n",
       "    ('volume', 'abs/2210.10606'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:252992545')],\n",
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       "    ('title', 'A Comprehensive Survey on Human Video Generation: Challenges, Methods, and Insights'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2024'), \n",
       "    ('volume', 'abs/2407.08428'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:271097523')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Lei, Wen-Ling'), Person('Wang, Jinting'), Person('Ma, Fengji'), Person('Huang, Guanjie'), Person('Liu, Li')])]))), ('shulei2024', Entry('article',\n",
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       "    ('title', 'A Survey on Deep Learning for Symbolic Music Generation: Representations, Algorithms, Evaluations, and Challenges'), \n",
       "    ('year', '2023'), \n",
       "    ('issue_date', 'January 2024'), \n",
       "    ('publisher', 'Association for Computing Machinery'), \n",
       "    ('address', 'New York, NY, USA'), \n",
       "    ('volume', '56'), \n",
       "    ('number', '1'), \n",
       "    ('issn', '0360-0300'), \n",
       "    ('url', 'https:\n",
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       "    ('abstract', 'Significant progress has been made in symbolic music generation with the help of deep learning techniques. However, the tasks covered by symbolic music generation have not been well summarized, and the evolution of generative models for the specific music generation task has not been illustrated systematically. This paper attempts to provide a task-oriented survey of symbolic music generation based on deep learning techniques, covering most of the currently popular music generation tasks. The distinct models under the same task are set forth briefly and strung according to their motivations, basically in chronological order. Moreover, we summarize the common datasets suitable for various tasks, discuss the music representations and the evaluation methods, highlight current challenges in symbolic music generation, and finally point out potential future research directions.'), \n",
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       "    ('articleno', '7'), \n",
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       "    ('keywords', 'music evaluation methods, symbolic music representations, deep learning, task-oriented survey, Symbolic music generation')],\n",
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       "    ('title', \"Between the AI and Me: Analysing Listeners' Perspectives on AI- and Human-Composed Progressive Metal Music\"), \n",
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       "    ('title', 'Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems'), \n",
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       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Rzevski, George'), Person('Langley, Pat'), Person('Simon, Herbert A.'), Person('Bradshaw, Gary L.'), Person('Zytkow, Jan M.')])]))), ('Deroski1993DiscoveringD', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Discovering Dynamics'), \n",
       "    ('booktitle', 'International Conference on Machine Learning'), \n",
       "    ('year', '1993'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:17202248')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Dzeroski, Saso'), Person('Todorovski, Ljupco')])]))), ('Koza1994GeneticPA', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Genetic programming as a means for programming computers by natural selection'), \n",
       "    ('journal', 'Statistics and Computing'), \n",
       "    ('year', '1994'), \n",
       "    ('volume', '4'), \n",
       "    ('pages', '87-112'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:8750149')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Koza, John R.')])]))), ('CoTodorovski1997DeclarativeBI', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Declarative Bias in Equation Discovery'), \n",
       "    ('year', '1997'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:7320694')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Todorovski, Ljup Co')])]))), ('lenat1984and', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Why AM and EURISKO appear to work'), \n",
       "    ('journal', 'Artificial intelligence'), \n",
       "    ('volume', '23'), \n",
       "    ('number', '3'), \n",
       "    ('pages', '269--294'), \n",
       "    ('year', '1984'), \n",
       "    ('publisher', 'Elsevier')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Lenat, Douglas B'), Person('Brown, John Seely')])]))), ('guimera2020bs', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'A Bayesian machine scientist to aid in the solution of challenging scientific problems'), \n",
       "    ('journal', 'Science Advances'), \n",
       "    ('volume', '6'), \n",
       "    ('number', '5'), \n",
       "    ('pages', 'eaav6971'), \n",
       "    ('year', '2020'), \n",
       "    ('doi', '10.1126/sciadv.aav6971'), \n",
       "    ('URL', 'https://www.science.org/doi/abs/10.1126/sciadv.aav6971'), \n",
       "    ('eprint', 'https://www.science.org/doi/pdf/10.1126/sciadv.aav6971'), \n",
       "    ('abstract', 'A Bayesian machine scientist uncovers closed-form mathematical models from data. Closed-form, interpretable mathematical models have been instrumental for advancing our understanding of the world; with the data revolution, we may now be in a position to uncover new such models for many systems from physics to the social sciences. However, to deal with increasing amounts of data, we need “machine scientists” that are able to extract these models automatically from data. Here, we introduce a Bayesian machine scientist, which establishes the plausibility of models using explicit approximations to the exact marginal posterior over models and establishes its prior expectations about models by learning from a large empirical corpus of mathematical expressions. It explores the space of models using Markov chain Monte Carlo. We show that this approach uncovers accurate models for synthetic and real data and provides out-of-sample predictions that are more accurate than those of existing approaches and of other nonparametric methods.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Guimerà, Roger'), Person('Reichardt, Ignasi'), Person('Aguilar-Mogas, Antoni'), Person('Massucci, Francesco A.'), Person('Miranda, Manuel'), Person('Pallarès, Jordi'), Person('Sales-Pardo, Marta')])]))), ('Petersen2019DeepSR', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients'), \n",
       "    ('journal', 'arXiv: Learning'), \n",
       "    ('year', '2019'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:212956516')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Petersen, Brenden K.'), Person('Landajuela, Mikel')])]))), ('Udrescu2020AIF2', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2020'), \n",
       "    ('volume', 'abs/2006.10782'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:219955930')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Udrescu, S. M.'), Person('Tan, Andrew Yong-Yi'), Person('Feng, Jiahai'), Person('Neto, Orisvaldo'), Person('Wu, Tailin'), Person('Tegmark, Max')])]))), ('Cranmer2020DiscoveringSM', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Discovering Symbolic Models from Deep Learning with Inductive Biases'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2020'), \n",
       "    ('volume', 'abs/2006.11287'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:219966125')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Cranmer, M.'), Person('Sanchez-Gonzalez, Alvaro'), Person('Battaglia, Peter W.'), Person('Xu, Rui'), Person('Cranmer, Kyle'), Person('Spergel, David N.'), Person('Ho, Shirley')])]))), ('Garcon2021DeepNN', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Deep neural networks to recover unknown physical parameters from oscillating time series'), \n",
       "    ('journal', 'PLoS ONE'), \n",
       "    ('year', '2021'), \n",
       "    ('volume', '17'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:231572959')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Garcon, Antoine'), Person('Vexler, Julian'), Person('Budker, Dmitry'), Person('Kramer, Stefan')])]))), ('Chen2022AutomatedDO', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Automated discovery of fundamental variables hidden in experimental data'), \n",
       "    ('journal', 'Nature Computational Science'), \n",
       "    ('year', '2022'), \n",
       "    ('volume', '2'), \n",
       "    ('pages', '433 - 442'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:251087119')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Chen, Boyuan'), Person('Huang, Kuang'), Person('Raghupathi, Sunand'), Person('Chandratreya, Ishaan Preetam'), Person('Du, Qi'), Person('Lipson, Hod')])]))), ('Raayoni_2021', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Generating conjectures on fundamental constants with the Ramanujan Machine'), \n",
       "    ('volume', '590'), \n",
       "    ('ISSN', '1476-4687'), \n",
       "    ('url', 'http://dx.doi.org/10.1038/s41586-021-03229-4'), \n",
       "    ('DOI', '10.1038/s41586-021-03229-4'), \n",
       "    ('number', '7844'), \n",
       "    ('journal', 'Nature'), \n",
       "    ('publisher', 'Springer Science and Business Media LLC'), \n",
       "    ('year', '2021'), \n",
       "    ('month', 'February'), \n",
       "    ('pages', '67–73')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Raayoni, Gal'), Person('Gottlieb, Shahar'), Person('Manor, Yahel'), Person('Pisha, George'), Person('Harris, Yoav'), Person('Mendlovic, Uri'), Person('Haviv, Doron'), Person('Hadad, Yaron'), Person('Kaminer, Ido')])]))), ('HAKUK2020101080', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Automated discovery of scientific concepts: Replicating three recent discoveries in mechanics'), \n",
       "    ('journal', 'Advanced Engineering Informatics'), \n",
       "    ('volume', '44'), \n",
       "    ('pages', '101080'), \n",
       "    ('year', '2020'), \n",
       "    ('issn', '1474-0346'), \n",
       "    ('doi', 'https://doi.org/10.1016/j.aei.2020.101080'), \n",
       "    ('url', 'https://www.sciencedirect.com/science/article/pii/S1474034620300495'), \n",
       "    ('keywords', 'Interdisciplinary engineering knowledge genome, Knowledge discovery, Scientific discovery, Truss mechanism duality, Knowledge-based discovery'), \n",
       "    ('abstract', 'Throughout history, humanity cherished new scientific discoveries; many people devoted their lives to find them in laborious manual processes. Since the emergence of artificial intelligence, about 60 years ago, the possibility of automated scientific discovery has become conceivable and has attracted significant and growing interest. Many studies of automated scientific discovery have been presented over the years. This article presents a new automated discovery approach that crosses disciplines and transfers knowledge between them. The approach requires rich and formal background knowledge to find concepts, methods, or laws not known in one discipline by using their counterpart in other disciplines, specifically disciplinary knowledge that is represented in the Interdisciplinary Engineering Knowledge Genome. Three recent discoveries in mechanics were replicated automatically through software execution, demonstrating the validity of the approach. In future studies, the goal is to discover new knowledge in mechanics and/or electronics, as well as venture into other disciplines including outside engineering.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Hakuk, Yaron'), Person('Reich, Yoram')])]))), ('FAJTLOWICZ1988113', Entry('incollection',\n",
       "  fields=[\n",
       "    ('title', 'On Conjectures of Graffiti'), \n",
       "    ('series', 'Annals of Discrete Mathematics'), \n",
       "    ('publisher', 'Elsevier'), \n",
       "    ('volume', '38'), \n",
       "    ('pages', '113-118'), \n",
       "    ('year', '1988'), \n",
       "    ('booktitle', 'Graph Theory and Applications'), \n",
       "    ('issn', '0167-5060'), \n",
       "    ('doi', 'https://doi.org/10.1016/S0167-5060(08)70776-3'), \n",
       "    ('url', 'https://www.sciencedirect.com/science/article/pii/S0167506008707763'), \n",
       "    ('abstract', 'Publisher Summary Graffiti is a computer program that makes graph-theoretical conjectures. The basic idea of Graffiti is that it “knows” certain graphs and it is capable of evaluating certain formulas formed from graph-theoretical invariants. If none of the graphs with which Graffiti is familiar is a counterexample to a formula then the formula is considered to be a conjecture. The number of conjectures, particularly those that are completely trivial, is the main problem and more than half of the program consists of various heuristics whose purpose is deletion of trivial and otherwise noninteresting but true conjectures. All conclusions that Graffiti draws are based exclusively on graphs it knows, so “follows” means of course “follows as far as the graphs in the library of program are concerned.” CNCL deletes those conjectures of the second and third type in which one of the invariants on the left is always smaller than an invariant on the right. This procedure may undoubtedly remove a number of interesting conjectures but it is the only one that seems to operate completely independently from IRIN.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('editor', [Person('Akiyama, J.'), Person('Egawa, Y.'), Person('Enomoto, H.')]), ('author', [Person('Fajtlowicz, Siemion')])]))), ('PhysRevLett2020', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Discovering Physical Concepts with Neural Networks'), \n",
       "    ('journal', 'Phys. Rev. Lett.'), \n",
       "    ('volume', '124'), \n",
       "    ('issue', '1'), \n",
       "    ('pages', '010508'), \n",
       "    ('numpages', '6'), \n",
       "    ('year', '2020'), \n",
       "    ('month', 'Jan'), \n",
       "    ('publisher', 'American Physical Society'), \n",
       "    ('doi', '10.1103/PhysRevLett.124.010508'), \n",
       "    ('url', 'https://link.aps.org/doi/10.1103/PhysRevLett.124.010508')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Iten, Raban'), Person('Metger, Tony'), Person('Wilming, Henrik'), Person(\"del Rio, L\\\\'{\\\\i}dia\"), Person('Renner, Renato')])]))), ('chen2016automated', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Automated discovery and proof of congruence theorems for partial sums of combinatorial sequences'), \n",
       "    ('journal', 'Journal of Difference Equations and Applications'), \n",
       "    ('volume', '22'), \n",
       "    ('number', '6'), \n",
       "    ('pages', '780--788'), \n",
       "    ('year', '2016'), \n",
       "    ('publisher', 'Taylor \\\\& Francis')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Chen, William YC'), Person('Hou, Qing-Hu'), Person('Zeilberger, Doron')])]))), ('wu2019toward', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Toward an artificial intelligence physicist for unsupervised learning'), \n",
       "    ('journal', 'Physical Review E'), \n",
       "    ('volume', '100'), \n",
       "    ('number', '3'), \n",
       "    ('pages', '033311'), \n",
       "    ('year', '2019'), \n",
       "    ('publisher', 'APS')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Wu, Tailin'), Person('Tegmark, Max')])]))), ('BUCHBERGER2006470', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Theorema: Towards computer-aided mathematical theory exploration'), \n",
       "    ('journal', 'Journal of Applied Logic'), \n",
       "    ('volume', '4'), \n",
       "    ('number', '4'), \n",
       "    ('pages', '470-504'), \n",
       "    ('year', '2006'), \n",
       "    ('note', 'Towards Computer Aided Mathematics'), \n",
       "    ('issn', '1570-8683'), \n",
       "    ('doi', 'https://doi.org/10.1016/j.jal.2005.10.006'), \n",
       "    ('url', 'https://www.sciencedirect.com/science/article/pii/S1570868305000716'), \n",
       "    ('keywords', 'Mathematical assistant, Automated reasoning, Theory exploration, “Lazy Thinking”, Theorema'), \n",
       "    ('abstract', 'Theorema is a project that aims at supporting the entire process of mathematical theory exploration within one coherent logic and software system. This survey paper illustrates the style of Theorema-supported mathematical theory exploration by a case study (the automated synthesis of an algorithm for the construction of Gröbner Bases) and gives an overview on some reasoners and organizational tools for theory exploration developed in the Theorema project.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Buchberger, Bruno'), Person('Crǎciun, Adrian'), Person('Jebelean, Tudor'), Person('Kovács, Laura'), Person('Kutsia, Temur'), Person('Nakagawa, Koji'), Person('Piroi, Florina'), Person('Popov, Nikolaj'), Person('Robu, Judit'), Person('Rosenkranz, Markus'), Person('Windsteiger, Wolfgang')])]))), ('lindsay1980applications', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Applications of artificial intelligence for organic chemistry: the DENDRAL project'), \n",
       "    ('journal', '(No Title)'), \n",
       "    ('year', '1980')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Lindsay, Robert K')])]))), ('schmidt2009distilling', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Distilling free-form natural laws from experimental data'), \n",
       "    ('journal', 'science'), \n",
       "    ('volume', '324'), \n",
       "    ('number', '5923'), \n",
       "    ('pages', '81--85'), \n",
       "    ('year', '2009'), \n",
       "    ('publisher', 'American Association for the Advancement of Science')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Schmidt, Michael'), Person('Lipson, Hod')])]))), ('savage2012automating', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Automating scientific discovery'), \n",
       "    ('journal', 'Communications of the ACM'), \n",
       "    ('volume', '55'), \n",
       "    ('number', '5'), \n",
       "    ('pages', '9--11'), \n",
       "    ('year', '2012'), \n",
       "    ('publisher', 'ACM New York, NY, USA')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Savage, Neil')])]))), ('Jumper2021', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Highly accurate protein structure prediction with AlphaFold'), \n",
       "    ('journal', 'Nature'), \n",
       "    ('year', '2021'), \n",
       "    ('month', 'Aug'), \n",
       "    ('day', '01'), \n",
       "    ('volume', '596'), \n",
       "    ('number', '7873'), \n",
       "    ('pages', '583-589'), \n",
       "    ('abstract', \"Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1--4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence---the structure prediction component of the `protein folding problem'8---has been an important open research problem for more than 50 years9. Despite recent progress10--14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.\"), \n",
       "    ('issn', '1476-4687'), \n",
       "    ('doi', '10.1038/s41586-021-03819-2'), \n",
       "    ('url', 'https://doi.org/10.1038/s41586-021-03819-2')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Jumper, John'), Person('Evans, Richard'), Person('Pritzel, Alexander'), Person('Green, Tim'), Person('Figurnov, Michael'), Person('Ronneberger, Olaf'), Person('Tunyasuvunakool, Kathryn'), Person('Bates, Russ'), Person(\"{\\\\v{Z}}{\\\\'i}dek, Augustin\"), Person('Potapenko, Anna'), Person('Bridgland, Alex'), Person('Meyer, Clemens'), Person('Kohl, Simon A. A.'), Person('Ballard, Andrew J.'), Person('Cowie, Andrew'), Person('Romera-Paredes, Bernardino'), Person('Nikolov, Stanislav'), Person('Jain, Rishub'), Person('Adler, Jonas'), Person('Back, Trevor'), Person('Petersen, Stig'), Person('Reiman, David'), Person('Clancy, Ellen'), Person('Zielinski, Michal'), Person('Steinegger, Martin'), Person('Pacholska, Michalina'), Person('Berghammer, Tamas'), Person('Bodenstein, Sebastian'), Person('Silver, David'), Person('Vinyals, Oriol'), Person('Senior, Andrew W.'), Person('Kavukcuoglu, Koray'), Person('Kohli, Pushmeet'), Person('Hassabis, Demis')])]))), ('zambaldi2024novodesignhighaffinityprotein', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'De novo design of high-affinity protein binders with AlphaProteo'), \n",
       "    ('year', '2024'), \n",
       "    ('eprint', '2409.08022'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'q-bio.BM'), \n",
       "    ('url', 'https://arxiv.org/abs/2409.08022')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Zambaldi, Vinicius'), Person('La, David'), Person('Chu, Alexander E.'), Person('Patani, Harshnira'), Person('Danson, Amy E.'), Person('Kwan, Tristan O. C.'), Person('Frerix, Thomas'), Person('Schneider, Rosalia G.'), Person('Saxton, David'), Person('Thillaisundaram, Ashok'), Person('Wu, Zachary'), Person('Moraes, Isabel'), Person('Lange, Oskar'), Person('Papa, Eliseo'), Person('Stanton, Gabriella'), Person('Martin, Victor'), Person('Singh, Sukhdeep'), Person('Wong, Lai H.'), Person('Bates, Russ'), Person('Kohl, Simon A.'), Person('Abramson, Josh'), Person('Senior, Andrew W.'), Person('Alguel, Yilmaz'), Person('Wu, Mary Y.'), Person('Aspalter, Irene M.'), Person('Bentley, Katie'), Person('Bauer, David L. V.'), Person('Cherepanov, Peter'), Person('Hassabis, Demis'), Person('Kohli, Pushmeet'), Person('Fergus, Rob'), Person('Wang, Jue')])]))), ('Trinh2024', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Solving olympiad geometry without human demonstrations'), \n",
       "    ('journal', 'Nature'), \n",
       "    ('year', '2024'), \n",
       "    ('month', 'Jan'), \n",
       "    ('day', '01'), \n",
       "    ('volume', '625'), \n",
       "    ('number', '7995'), \n",
       "    ('pages', '476-482'), \n",
       "    ('abstract', \"Proving mathematical theorems at the olympiad level represents a notable milestone in human-level automated reasoning1--4, owing to their reputed difficulty among the world's best talents in pre-university mathematics. Current machine-learning approaches, however, are not applicable to most mathematical domains owing to the high cost of translating human proofs into machine-verifiable format. The problem is even worse for geometry because of its unique translation challenges1,5, resulting in severe scarcity of training data. We propose AlphaGeometry, a theorem prover for Euclidean plane geometry that sidesteps the need for human demonstrations by synthesizing millions of theorems and proofs across different levels of complexity. AlphaGeometry is a neuro-symbolic system that uses a neural language model, trained from scratch on our large-scale synthetic data, to guide a symbolic deduction engine through infinite branching points in challenging problems. On a test set of 30 latest olympiad-level problems, AlphaGeometry solves 25, outperforming the previous best method that only solves ten problems and approaching the performance of an average International Mathematical Olympiad (IMO) gold medallist. Notably, AlphaGeometry produces human-readable proofs, solves all geometry problems in the IMO 2000 and 2015 under human expert evaluation and discovers a generalized version of a translated IMO theorem in 2004.\"), \n",
       "    ('issn', '1476-4687'), \n",
       "    ('doi', '10.1038/s41586-023-06747-5'), \n",
       "    ('url', 'https://doi.org/10.1038/s41586-023-06747-5')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Trinh, Trieu H.'), Person('Wu, Yuhuai'), Person('Le, Quoc V.'), Person('He, He'), Person('Luong, Thang')])]))), ('hubert2024', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'AlphaProof'), \n",
       "    ('year', '2024'), \n",
       "    ('url', 'https://deepmind.google/discover/blog/ai-solves-imo-problems-at-silver-medal-level/')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Hubert, Thomas'), Person('Mehta, Rishi'), Person('Sartran, Laurent')])]))), ('Abramson2024', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Accurate structure prediction of biomolecular interactions with AlphaFold 3'), \n",
       "    ('journal', 'Nature'), \n",
       "    ('year', '2024'), \n",
       "    ('month', 'Jun'), \n",
       "    ('day', '01'), \n",
       "    ('volume', '630'), \n",
       "    ('number', '8016'), \n",
       "    ('pages', '493-500'), \n",
       "    ('abstract', 'The introduction of AlphaFold{\\\\thinspace}21 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design2--6. Here we describe our AlphaFold{\\\\thinspace}3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein--ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein--nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody--antigen prediction accuracy compared with AlphaFold-Multimer v.2.37,8. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.'), \n",
       "    ('issn', '1476-4687'), \n",
       "    ('doi', '10.1038/s41586-024-07487-w'), \n",
       "    ('url', 'https://doi.org/10.1038/s41586-024-07487-w')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Abramson, Josh'), Person('Adler, Jonas'), Person('Dunger, Jack'), Person('Evans, Richard'), Person('Green, Tim'), Person('Pritzel, Alexander'), Person('Ronneberger, Olaf'), Person('Willmore, Lindsay'), Person('Ballard, Andrew J.'), Person('Bambrick, Joshua'), Person('Bodenstein, Sebastian W.'), Person('Evans, David A.'), Person('Hung, Chia-Chun'), Person(\"O'Neill, Michael\"), Person('Reiman, David'), Person('Tunyasuvunakool, Kathryn'), Person('Wu, Zachary'), Person('{\\\\v{Z}}emgulyt{\\\\.{e}}, Akvil{\\\\.{e}}'), Person('Arvaniti, Eirini'), Person('Beattie, Charles'), Person('Bertolli, Ottavia'), Person('Bridgland, Alex'), Person('Cherepanov, Alexey'), Person('Congreve, Miles'), Person('Cowen-Rivers, Alexander I.'), Person('Cowie, Andrew'), Person('Figurnov, Michael'), Person('Fuchs, Fabian B.'), Person('Gladman, Hannah'), Person('Jain, Rishub'), Person('Khan, Yousuf A.'), Person('Low, Caroline M. R.'), Person('Perlin, Kuba'), Person('Potapenko, Anna'), Person('Savy, Pascal'), Person('Singh, Sukhdeep'), Person('Stecula, Adrian'), Person('Thillaisundaram, Ashok'), Person('Tong, Catherine'), Person('Yakneen, Sergei'), Person('Zhong, Ellen D.'), Person('Zielinski, Michal'), Person(\"{\\\\v{Z}}{\\\\'i}dek, Augustin\"), Person('Bapst, Victor'), Person('Kohli, Pushmeet'), Person('Jaderberg, Max'), Person('Hassabis, Demis'), Person('Jumper, John M.')])]))), ('si2024llmsgeneratenovelresearch', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers'), \n",
       "    ('year', '2024'), \n",
       "    ('eprint', '2409.04109'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL'), \n",
       "    ('url', 'https://arxiv.org/abs/2409.04109')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Si, Chenglei'), Person('Yang, Diyi'), Person('Hashimoto, Tatsunori')])]))), ('Baek2024ResearchAgentIR', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2024'), \n",
       "    ('volume', 'abs/2404.07738'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:269042844')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Baek, Jinheon'), Person('Jauhar, Sujay Kumar'), Person('Cucerzan, Silviu'), Person('Hwang, Sung Ju')])]))), ('skarlinski2024language', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Language agents achieve superhuman synthesis of scientific knowledge'), \n",
       "    ('year', '2024'), \n",
       "    ('journal', 'preprint'), \n",
       "    ('url', 'https://paper.wikicrow.ai')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Skarlinski, Michael D.'), Person('Cox, Sam'), Person('Laurent, Jon M.'), Person('Braza, James D.'), Person('Hinks, Michaela'), Person('Hammerling, Michael J.'), Person('Ponnapati, Manvitha'), Person('Rodriques, Samuel G.'), Person('White, Andrew D.')])]))), ('wang2024scimonscientificinspirationmachines', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'SciMON: Scientific Inspiration Machines Optimized for Novelty'), \n",
       "    ('year', '2024'), \n",
       "    ('eprint', '2305.14259'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL'), \n",
       "    ('url', 'https://arxiv.org/abs/2305.14259')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Wang, Qingyun'), Person('Downey, Doug'), Person('Ji, Heng'), Person('Hope, Tom')])]))), ('qi2023largelanguagemodelszero', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Large Language Models are Zero Shot Hypothesis Proposers'), \n",
       "    ('year', '2023'), \n",
       "    ('eprint', '2311.05965'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL'), \n",
       "    ('url', 'https://arxiv.org/abs/2311.05965')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Qi, Biqing'), Person('Zhang, Kaiyan'), Person('Li, Haoxiang'), Person('Tian, Kai'), Person('Zeng, Sihang'), Person('Chen, Zhang-Ren'), Person('Zhou, Bowen')])]))), ('Yang2023LargeLM', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Large Language Models for Automated Open-domain Scientific Hypotheses Discovery'), \n",
       "    ('booktitle', 'Annual Meeting of the Association for Computational Linguistics'), \n",
       "    ('year', '2023'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:261557055')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Yang, Zonglin'), Person('Du, Xinya'), Person('Li, Junxian'), Person('Zheng, Jie'), Person('Poria, Soujanya'), Person('Cambria, E.')])]))), ('Wang2023SciMONSI', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'SciMON: Scientific Inspiration Machines Optimized for Novelty'), \n",
       "    ('booktitle', 'Annual Meeting of the Association for Computational Linguistics'), \n",
       "    ('year', '2023'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:258841365')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Wang, Qingyun'), Person('Downey, Doug'), Person('Ji, Heng'), Person('Hope, Tom')])]))), ('Ghafarollahi2024SciAgentsAS', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning'), \n",
       "    ('year', '2024'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:272524655')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Ghafarollahi, Alireza'), Person('Buehler, Markus J.')])]))), ('Li2024MLRCopilotAM', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agents'), \n",
       "    ('year', '2024'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:271957477')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Li, Ruochen'), Person('Patel, Teerth'), Person('Wang, Qingyun'), Person('Wang, Qingyun'), Person('Du, Xinya')])]))), ('lu2024aiscientistfullyautomated', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery'), \n",
       "    ('year', '2024'), \n",
       "    ('eprint', '2408.06292'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.AI'), \n",
       "    ('url', 'https://arxiv.org/abs/2408.06292')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Lu, Chris'), Person('Lu, Cong'), Person('Lange, Robert Tjarko'), Person('Foerster, Jakob'), Person('Clune, Jeff'), Person('Ha, David')])]))), ('liu2024coquest', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'How AI Processing Delays Foster Creativity: Exploring Research Question Co-Creation with an LLM-based Agent'), \n",
       "    ('year', '2024'), \n",
       "    ('isbn', '9798400703300'), \n",
       "    ('publisher', 'Association for Computing Machinery'), \n",
       "    ('address', 'New York, NY, USA'), \n",
       "    ('url', 'https:\n",
       "    //doi.org/10.1145/3613904.3642698'), ('doi', '10.1145/3613904.3642698'), \n",
       "    ('abstract', 'Developing novel research questions (RQs) often requires extensive literature reviews, especially in interdisciplinary fields. To support RQ development through human-AI co-creation, we leveraged Large Language Models (LLMs) to build an LLM-based agent system named CoQuest. We conducted an experiment with 20 HCI researchers to examine the impact of two interaction designs: breadth-first and depth-first RQ generation. The findings revealed that participants perceived the breadth-first approach as more creative and trustworthy upon task completion. Conversely, during the task, participants considered the depth-first generated RQs as more creative. Additionally, we discovered that AI processing delays allowed users to reflect on multiple RQs simultaneously, leading to a higher quantity of generated RQs and an enhanced sense of control. Our work makes both theoretical and practical contributions by proposing and evaluating a mental model for human-AI co-creation of RQs. We also address potential ethical issues, such as biases and over-reliance on AI, advocating for using the system to improve human research creativity rather than automating scientific inquiry. The system’s source is available at: https://github.com/yiren-liu/coquest.'), \n",
       "    ('booktitle', 'Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems'), \n",
       "    ('articleno', '17'), \n",
       "    ('numpages', '25'), \n",
       "    ('keywords', 'Co-creation Systems, Large Language Models, Mixed-initiative Design, Scientifc Discovery'), \n",
       "    ('location', 'Honolulu, HI, USA'), \n",
       "    ('series', \"CHI '24\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Liu, Yiren'), Person('Chen, Si'), Person('Cheng, Haocong'), Person('Yu, Mengxia'), Person('Ran, Xiao'), Person('Mo, Andrew'), Person('Tang, Yiliu'), Person('Huang, Yun')])]))), ('king2009automate', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'The Automation of Science'), \n",
       "    ('journal', 'Science'), \n",
       "    ('volume', '324'), \n",
       "    ('number', '5923'), \n",
       "    ('pages', '85-89'), \n",
       "    ('year', '2009'), \n",
       "    ('doi', '10.1126/science.1165620'), \n",
       "    ('URL', 'https://www.science.org/doi/abs/10.1126/science.1165620'), \n",
       "    ('eprint', 'https://www.science.org/doi/pdf/10.1126/science.1165620'), \n",
       "    ('abstract', \"The basis of science is the hypothetico-deductive method and the recording of experiments in sufficient detail to enable reproducibility. We report the development of Robot Scientist “Adam,” which advances the automation of both. Adam has autonomously generated functional genomics hypotheses about the yeast Saccharomyces cerevisiae and experimentally tested these hypotheses by using laboratory automation. We have confirmed Adam's conclusions through manual experiments. To describe Adam's research, we have developed an ontology and logical language. The resulting formalization involves over 10,000 different research units in a nested treelike structure, 10 levels deep, that relates the 6.6 million biomass measurements to their logical description. This formalization describes how a machine contributed to scientific knowledge.\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('King, Ross D.'), Person('Rowland, Jem'), Person('Oliver, Stephen G.'), Person('Young, Michael'), Person('Aubrey, Wayne'), Person('Byrne, Emma'), Person('Liakata, Maria'), Person('Markham, Magdalena'), Person('Pir, Pinar'), Person('Soldatova, Larisa N.'), Person('Sparkes, Andrew'), Person('Whelan, Kenneth E.'), Person('Clare, Amanda')])]))), ('waltz2009automate', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Automating Science'), \n",
       "    ('journal', 'Science'), \n",
       "    ('volume', '324'), \n",
       "    ('number', '5923'), \n",
       "    ('pages', '43-44'), \n",
       "    ('year', '2009'), \n",
       "    ('doi', '10.1126/science.1172781'), \n",
       "    ('URL', 'https://www.science.org/doi/abs/10.1126/science.1172781'), \n",
       "    ('eprint', 'https://www.science.org/doi/pdf/10.1126/science.1172781')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Waltz, David'), Person('Buchanan, Bruce G.')])]))), ('ifargan2024autonomousllmdrivenresearchdata', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Autonomous LLM-driven research from data to human-verifiable research papers'), \n",
       "    ('year', '2024'), \n",
       "    ('eprint', '2404.17605'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'q-bio.OT'), \n",
       "    ('url', 'https://arxiv.org/abs/2404.17605')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Ifargan, Tal'), Person('Hafner, Lukas'), Person('Kern, Maor'), Person('Alcalay, Ori'), Person('Kishony, Roy')])]))), ('majumder2024discoverybenchdatadrivendiscoverylarge', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'DiscoveryBench: Towards Data-Driven Discovery with Large Language Models'), \n",
       "    ('year', '2024'), \n",
       "    ('eprint', '2407.01725'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL'), \n",
       "    ('url', 'https://arxiv.org/abs/2407.01725')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Majumder, Bodhisattwa Prasad'), Person('Surana, Harshit'), Person('Agarwal, Dhruv'), Person('Mishra, Bhavana Dalvi'), Person('Meena, Abhijeetsingh'), Person('Prakhar, Aryan'), Person('Vora, Tirth'), Person('Khot, Tushar'), Person('Sabharwal, Ashish'), Person('Clark, Peter')])]))), ('Girotra2023IdeasAD', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Ideas are Dimes a Dozen: Large Language Models for Idea Generation in Innovation'), \n",
       "    ('journal', 'SSRN Electronic Journal'), \n",
       "    ('year', '2023'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:260467886')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Girotra, Karan'), Person('Meincke, Lennart'), Person('Terwiesch, Christian'), Person('Ulrich, Karl T.')])]))), ('boiko2023emergentautonomousscientificresearch', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Emergent autonomous scientific research capabilities of large language models'), \n",
       "    ('year', '2023'), \n",
       "    ('eprint', '2304.05332'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'physics.chem-ph'), \n",
       "    ('url', 'https://arxiv.org/abs/2304.05332')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Boiko, Daniil A.'), Person('MacKnight, Robert'), Person('Gomes, Gabe')])]))), ('Wang2019PaperRobotID', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'PaperRobot: Incremental Draft Generation of Scientific Ideas'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2019'), \n",
       "    ('volume', 'abs/1905.07870'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:159040684')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Wang, Qingyun'), Person('Huang, Lifu'), Person('Jiang, Zhiying'), Person('Knight, Kevin'), Person('Ji, Heng'), Person('Bansal, Mohit'), Person('Luan, Yi')])]))), ('altmae2023', Entry('article',\n",
       "  fields=[\n",
       "    ('year', '2023'), \n",
       "    ('month', '04'), \n",
       "    ('pages', ''), \n",
       "    ('title', 'Artificial intelligence in scientific writing: a friend or a foe?'), \n",
       "    ('volume', '47'), \n",
       "    ('journal', 'Reproductive BioMedicine Online'), \n",
       "    ('doi', '10.1016/j.rbmo.2023.04.009')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Altmäe, Signe'), Person('Sola-Leyva, Alberto'), Person('Salumets, Andres')])]))), ('Sybrandt2020', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'AGATHA: Automatic Graph Mining And Transformer based Hypothesis Generation Approach'), \n",
       "    ('year', '2020'), \n",
       "    ('isbn', '9781450368599'), \n",
       "    ('publisher', 'Association for Computing Machinery'), \n",
       "    ('address', 'New York, NY, USA'), \n",
       "    ('url', 'https:\n",
       "    //doi.org/10.1145/3340531.3412684'), ('doi', '10.1145/3340531.3412684'), \n",
       "    ('abstract', \"Medical research is risky and expensive. Drug discovery requires researchers to efficiently winnow thousands of potential targets to a small candidate set. However, scientists spend significant time and money long before seeing the intermediate results that ultimately determine this smaller set. Hypothesis generation systems address this challenge by mining the wealth of publicly available scientific information to predict plausible research directions. We present AGATHA, a deep-learning hypothesis generation system that learns a data-driven ranking criteria to recommend new biomedical connections. We massively validate our system with a temporal holdout wherein we predict connections first introduced after 2015 using data published beforehand. We additionally explore biomedical sub-domains, and demonstrate AGATHA's predictive capacity across the twenty most popular relationship types. Furthermore, we perform an ablation study to examine the aspects of our semantic network that most contribute to recommendation quality. Overall, AGATHA achieves best-in-class recommendation quality when compared to other hypothesis generation systems built to predict across all available biomedical literature. Reproducibility: All code, experimental data, and pre-trained models are available online: sybrandt.com/2020/agatha.\"), \n",
       "    ('booktitle', 'Proceedings of the 29th ACM International Conference on Information \\\\& Knowledge Management'), \n",
       "    ('pages', '2757–2764'), \n",
       "    ('numpages', '8'), \n",
       "    ('keywords', 'biomedical recommendation, hypothesis generation, literature-based discovery, semantic networks, transformer models'), \n",
       "    ('location', 'Virtual Event, Ireland'), \n",
       "    ('series', \"CIKM '20\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Sybrandt, Justin'), Person('Tyagin, Ilya'), Person('Shtutman, Michael'), Person('Safro, Ilya')])]))), ('Li2021FromKT', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'From Kepler to Newton: Explainable AI for Science Discovery'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2021'), \n",
       "    ('volume', 'abs/2111.12210'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:244728403')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Li, Zelong'), Person('Ji, Jianchao'), Person('Zhang, Yongfeng')])]))), ('Ulam1958JohnVN', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'John von Neumann 1903-1957'), \n",
       "    ('journal', 'Bulletin of the American Mathematical Society'), \n",
       "    ('year', '1958'), \n",
       "    ('volume', '64'), \n",
       "    ('pages', '1-49'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:122374268')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Ulam, Stanisław')])]))), ('Good1965SpeculationsCT', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Speculations Concerning the First Ultraintelligent Machine'), \n",
       "    ('journal', 'Adv. Comput.'), \n",
       "    ('year', '1965'), \n",
       "    ('volume', '6'), \n",
       "    ('pages', '31-88'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:17886872')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Good, I. J.')])]))), ('huang2024mlagentbenchevaluatinglanguageagents', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation'), \n",
       "    ('year', '2024'), \n",
       "    ('eprint', '2310.03302'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.LG'), \n",
       "    ('url', 'https://arxiv.org/abs/2310.03302')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Huang, Qian'), Person('Vora, Jian'), Person('Liang, Percy'), Person('Leskovec, Jure')])]))), ('Colton2008CreativityVT', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Creativity Versus the Perception of Creativity in Computational Systems'), \n",
       "    ('booktitle', 'AAAI Spring Symposium: Creative Intelligent Systems'), \n",
       "    ('year', '2008'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:2876679')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Colton, Simon')])]))), ('lamb2018eval', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Evaluating Computational Creativity: An Interdisciplinary Tutorial'), \n",
       "    ('year', '2018'), \n",
       "    ('issue_date', 'March 2019'), \n",
       "    ('publisher', 'Association for Computing Machinery'), \n",
       "    ('address', 'New York, NY, USA'), \n",
       "    ('volume', '51'), \n",
       "    ('number', '2'), \n",
       "    ('issn', '0360-0300'), \n",
       "    ('url', 'https:\n",
       "    //doi.org/10.1145/3167476'), ('doi', '10.1145/3167476'), \n",
       "    ('abstract', 'This article is a tutorial for researchers who are designing software to perform a creative task and want to evaluate their system using interdisciplinary theories of creativity. Researchers who study human creativity have a great deal to offer computational creativity. We summarize perspectives from psychology, philosophy, cognitive science, and computer science as to how creativity can be measured both in humans and in computers. We survey how these perspectives have been used in computational creativity research and make recommendations for how they should be used.'), \n",
       "    ('journal', 'ACM Comput. Surv.'), \n",
       "    ('month', 'February'), \n",
       "    ('articleno', '28'), \n",
       "    ('numpages', '34'), \n",
       "    ('keywords', 'digital art, computational creativity, Computational aesthetics')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Lamb, Carolyn'), Person('Brown, Daniel G.'), Person('Clarke, Charles L. A.')])]))), ('gervas2019', Entry('inbook',\n",
       "  fields=[\n",
       "    ('year', '2019'), \n",
       "    ('month', '07'), \n",
       "    ('pages', '275-304'), \n",
       "    ('title', 'Exploring Quantitative Evaluations of the Creativity of Automatic Poets'), \n",
       "    ('isbn', '978-3-319-43608-1'), \n",
       "    ('doi', '10.1007/978-3-319-43610-4_13')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Gervás, Pablo')])]))), ('Veale2015GameOT', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Game of Tropes: Exploring the Placebo Effect in Computational Creativity'), \n",
       "    ('booktitle', 'International Conference on Innovative Computing and Cloud Computing'), \n",
       "    ('year', '2015'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:3966712')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Veale, Tony')])]))), ('Baer2012DomainSA', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Domain Specificity and the Limits of Creativity Theory'), \n",
       "    ('journal', 'Journal of Creative Behavior'), \n",
       "    ('year', '2012'), \n",
       "    ('volume', '46'), \n",
       "    ('pages', '16-29'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:45983030')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Baer, John')])]))), ('Minsky1982WhyPT', Entry('article',\n",
       "  fields=[\n",
       "    ('title', \"Why People Think Computers Can't\"), \n",
       "    ('journal', 'AI Mag.'), \n",
       "    ('year', '1982'), \n",
       "    ('volume', '3'), \n",
       "    ('pages', '3-15'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:42565554')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Minsky, Marvin')])]))), ('Boden1991TheCM', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'The creative mind : myths \\\\& mechanisms'), \n",
       "    ('year', '1991'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:143261160')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Boden, Margaret A.')])]))), ('Karampiperis2014TowardsMF', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Towards Machines for Measuring Creativity: The Use of Computational Tools in Storytelling Activities'), \n",
       "    ('journal', '2014 IEEE 14th International Conference on Advanced Learning Technologies'), \n",
       "    ('year', '2014'), \n",
       "    ('pages', '508-512'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:17903634')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Karampiperis, Pythagoras'), Person('Koukourikos, Antonis'), Person('Koliopoulou, Evangelia')])]))), ('Jordanous2016TheLT', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'The longer term value of creativity judgements in computational creativity'), \n",
       "    ('year', '2016'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:637189')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Jordanous, Anna')])]))), ('peeperkorn2024temperaturecreativityparameterlarge', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Is Temperature the Creativity Parameter of Large Language Models?'), \n",
       "    ('year', '2024'), \n",
       "    ('eprint', '2405.00492'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL'), \n",
       "    ('url', 'https://arxiv.org/abs/2405.00492')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Peeperkorn, Max'), Person('Kouwenhoven, Tom'), Person('Brown, Dan'), Person('Jordanous, Anna')])]))), ('banerjee2024llmshallucinateneedlive', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'LLMs Will Always Hallucinate, and We Need to Live With This'), \n",
       "    ('year', '2024'), \n",
       "    ('eprint', '2409.05746'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'stat.ML'), \n",
       "    ('url', 'https://arxiv.org/abs/2409.05746')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Banerjee, Sourav'), Person('Agarwal, Ayushi'), Person('Singla, Saloni')])]))), ('Carlini2022QuantifyingMA', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Quantifying Memorization Across Neural Language Models'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2022'), \n",
       "    ('volume', 'abs/2202.07646'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:246863735')],\n",
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       "  fields=[\n",
       "    ('title', 'A taxonomy and review of generalization research in NLP'), \n",
       "    ('journal', 'Nature Machine Intelligence'), \n",
       "    ('year', '2022'), \n",
       "    ('volume', '5'), \n",
       "    ('pages', '1161 - 1174'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:252735124')],\n",
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       "    ('title', 'The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks'), \n",
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       "    ('journal', 'ArXiv'), \n",
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       "    ('title', \"Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs\"), \n",
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       "  fields=[\n",
       "    ('title', 'Disrupting Creativity: Copyright Law in the Age of Generative Artificial Intelligence'), \n",
       "    ('journal', 'Elsevier'), \n",
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       "  fields=[\n",
       "    ('title', 'The Times Sues OpenAI and Microsoft Over A.I. Use of Copyrighted Work.'), \n",
       "    ('journal', 'New York Times'), \n",
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       "    ('url', 'https://www.nytimes.com/2023/12/27/business/media/new-york-times-open-ai-microsoft-lawsuit.html')],\n",
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       "    ('title', 'Artists take new shot at Stability, Midjourney in updated copyright lawsuit.'), \n",
       "    ('journal', 'Reuters'), \n",
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       "    ('url', 'https://www.reuters.com/legal/litigation/artists-take-new-shot-stability-midjourney-updated-copyright-lawsuit-2023-11-30/')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Brittain, B.')])]))), ('Netanel2011MakingSO', Entry('inproceedings',\n",
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       "    ('title', 'Making Sense of Fair Use'), \n",
       "    ('year', '2011'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:152743703')],\n",
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       "  fields=[\n",
       "    ('title', \"Artificial Intelligence's Fair Use Crisis\"), \n",
       "    ('journal', 'Columbia Journal of Law and the Arts'), \n",
       "    ('year', '2017'), \n",
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       "    ('url', 'https://api.semanticscholar.org/CorpusID:115500744')],\n",
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       "    ('title', 'People Not Machines: Authorship and What It Means in the Berne Convention'), \n",
       "    ('journal', 'IIC - International Review of Intellectual Property and Competition Law'), \n",
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       "    ('title', 'COPYRIGHT PROTECTION FOR COMPUTER PROGRAMS, DATABASES, AND COMPUTER-GENERATED WORKS: IS ANYTHING NEW SINCE CONTU?'), \n",
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       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Miller, Arthur R.')])]))), ('Samuelson1986AllocatingOR', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Allocating Ownership Rights in Computer-Generated Works'), \n",
       "    ('journal', 'University of Pittsburgh Law Review'), \n",
       "    ('year', '1986'), \n",
       "    ('volume', '47'), \n",
       "    ('pages', '1185'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:142911255')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Samuelson, Pamela')])]))), ('Denicola2016ExMC', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Ex Machina: Copyright Protection for Computer-Generated Works'), \n",
       "    ('journal', 'Innovation Law \\\\& Policy eJournal'), \n",
       "    ('year', '2016'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:88486949')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Denicola, Robert C.')])]))), ('ralston2005', Entry('article',\n",
       "  fields=[\n",
       "    ('year', '2005'), \n",
       "    ('month', '03'), \n",
       "    ('pages', '281-284'), \n",
       "    ('title', 'Copyright in computer-composed music: Hal meets Handel'), \n",
       "    ('volume', '52'), \n",
       "    ('journal', 'Journal of the Copyright Society of the U.S.A')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Ralston, W.T.')])]))), ('Santos2020IntellectualPO', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Intellectual Property on Works of Art Made by Artificial Intelligence'), \n",
       "    ('journal', 'International Journal of Advanced Engineering Research and Science'), \n",
       "    ('year', '2020'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:234541178')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person(\"dos Santos, Cl{\\\\'a}udio Lisboa\"), Person('Machado, {\\\\^A}ngela Rocha')])]))), ('Deltorn2018AuthorshipIT', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Authorship in the Age of Machine learning and Artificial Intelligence'), \n",
       "    ('journal', 'Legal Perspectives in Information Systems eJournal'), \n",
       "    ('year', '2018'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:69806575')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Deltorn, Jean-Marc'), Person('Macrez, Franck')])]))), ('Guadamuz2017DoAD', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Do Androids Dream of Electric Copyright? Comparative Analysis of Originality in Artificial Intelligence Generated Works'), \n",
       "    ('journal', 'Econometrics: Computer Programs \\\\& Software eJournal'), \n",
       "    ('year', '2017'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:21670175')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Guadamuz, Andres')])]))), ('McCoy2023EmbersOA', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2023'), \n",
       "    ('volume', 'abs/2309.13638'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:262464572')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('McCoy, R. Thomas'), Person('Yao, Shunyu'), Person('Friedman, Dan'), Person('Hardy, Matthew'), Person('Griffiths, Thomas L.')])]))), ('taylor2004computationally', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Computationally recognizing wordplay in jokes'), \n",
       "    ('booktitle', 'Proceedings of the Annual Meeting of the Cognitive Science Society'), \n",
       "    ('volume', '26'), \n",
       "    ('number', '26'), \n",
       "    ('year', '2004')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Taylor, Julia M'), Person('Mazlack, Lawrence J')])]))), ('liu-etal-2018-exploiting', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Exploiting Syntactic Structures for Humor Recognition'), \n",
       "    ('booktitle', 'Proceedings of the 27th International Conference on Computational Linguistics'), \n",
       "    ('month', 'August'), \n",
       "    ('year', '2018'), \n",
       "    ('address', 'Santa Fe, New Mexico, USA'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/C18-1159'), \n",
       "    ('pages', '1875--1883'), \n",
       "    ('abstract', 'Humor recognition is an interesting and challenging task in natural language processing. This paper proposes to exploit syntactic structure features to enhance humor recognition. Our method achieves significant improvements compared with humor theory driven baselines. We found that some syntactic structure features consistently correlate with humor, which indicate interesting linguistic phenomena. Both the experimental results and the analysis demonstrate that humor can be viewed as a kind of style and content independent syntactic structures can help identify humor and have good interpretability.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Liu, Lizhen'), Person('Zhang, Donghai'), Person('Song, Wei')]), ('editor', [Person('Bender, Emily M.'), Person('Derczynski, Leon'), Person('Isabelle, Pierre')])]))), ('abulaish2020', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'A Survey of Figurative Language and Its Computational Detection in Online Social Networks'), \n",
       "    ('year', '2020'), \n",
       "    ('issue_date', 'February 2020'), \n",
       "    ('publisher', 'Association for Computing Machinery'), \n",
       "    ('address', 'New York, NY, USA'), \n",
       "    ('volume', '14'), \n",
       "    ('number', '1'), \n",
       "    ('issn', '1559-1131'), \n",
       "    ('url', 'https:\n",
       "    //doi.org/10.1145/3375547'), ('doi', '10.1145/3375547'), \n",
       "    ('abstract', 'The frequent usage of figurative language on online social networks, especially on Twitter, has the potential to mislead traditional sentiment analysis and recommender systems. Due to the extensive use of slangs, bashes, flames, and non-literal texts, tweets are a great source of figurative language, such as sarcasm, irony, metaphor, simile, hyperbole, humor, and satire. Starting with a brief introduction of figurative language and its various categories, this article presents an in-depth survey of the state-of-the-art techniques for computational detection of seven different figurative language categories, mainly on Twitter. For each figurative language category, we present details about the characterizing features, datasets, and state-of-the-art computational detection approaches. Finally, we discuss open challenges and future directions of research for each figurative language category.'), \n",
       "    ('journal', 'ACM Trans. Web'), \n",
       "    ('month', 'February'), \n",
       "    ('articleno', '3'), \n",
       "    ('numpages', '52'), \n",
       "    ('keywords', 'Social network analysis, figurative language, humor recognition, hyperbole detection, irony detection, metaphor detection, sarcasm detection, satire detection, simile detection')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Abulaish, Muhammad'), Person('Kamal, Ashraf'), Person('Zaki, Mohammed J.')])]))), ('zhang-etal-2019-telling', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Telling the Whole Story: A Manually Annotated {C}hinese Dataset for the Analysis of Humor in Jokes'), \n",
       "    ('booktitle', 'Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)'), \n",
       "    ('month', 'November'), \n",
       "    ('year', '2019'), \n",
       "    ('address', 'Hong Kong, China'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/D19-1673'), \n",
       "    ('doi', '10.18653/v1/D19-1673'), \n",
       "    ('pages', '6402--6407'), \n",
       "    ('abstract', 'Humor plays important role in human communication, which makes it important problem for natural language processing. Prior work on the analysis of humor focuses on whether text is humorous or not, or the degree of funniness, but this is insufficient to explain why it is funny. We therefore create a dataset on humor with 9,123 manually annotated jokes in Chinese. We propose a novel annotation scheme to give scenarios of how humor arises in text. Specifically, our annotations of linguistic humor not only contain the degree of funniness, like previous work, but they also contain key words that trigger humor as well as character relationship, scene, and humor categories. We report reasonable agreement between annota-tors. We also conduct an analysis and exploration of the dataset. To the best of our knowledge, we are the first to approach humor annotation for exploring the underlying mechanism of the use of humor, which may contribute to a significantly deeper analysis of humor. We also contribute with a scarce and valuable dataset, which we will release publicly.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Zhang, Dongyu'), Person('Zhang, Heting'), Person('Liu, Xikai'), Person('Lin, Hongfei'), Person('Xia, Feng')]), ('editor', [Person('Inui, Kentaro'), Person('Jiang, Jing'), Person('Ng, Vincent'), Person('Wan, Xiaojun')])]))), ('ziser2020', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Humor Detection in Product Question Answering Systems'), \n",
       "    ('year', '2020'), \n",
       "    ('isbn', '9781450380164'), \n",
       "    ('publisher', 'Association for Computing Machinery'), \n",
       "    ('address', 'New York, NY, USA'), \n",
       "    ('url', 'https:\n",
       "    //doi.org/10.1145/3397271.3401077'), ('doi', '10.1145/3397271.3401077'), \n",
       "    ('abstract', 'Community question-answering (CQA) has been established as a prominent web service enabling users to post questions and get answers from the community. Product Question Answering (PQA) is a special CQA framework where questions are asked (and are answered) in the context of a specific product. Naturally, humorous questions are integral part of such platforms, especially as some products attract humor due to their unreasonable price, their peculiar functionality, or in cases that users emphasize their critical point-of-view through humor. Detecting humorous questions in such systems is important for sellers, to better understand user engagement with their products. It is also important to signal users about flippancy of humorous questions, and that answers for such questions should be taken with a grain of salt.In this study we present a deep-learning framework for detecting humorous questions in PQA systems. Our framework utilizes two properties of the questions - Incongruity and Subjectivity, demonstrating their contribution for humor detection. We evaluate our framework over a real-world dataset, demonstrating an accuracy of 90.8\\\\%, up to 18.3\\\\% relative improvement over baseline methods. We then demonstrate the existence of product bias in PQA platforms, when some products attract more humorous questions than others. A classifier trained over unbiased data is outperformed by the biased classifier, however, it excels in the task of differentiating between humorous and non-humorous questions that are both related to the same product. To the best of our knowledge this work is the first to detect humor in PQA setting.'), \n",
       "    ('booktitle', 'Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval'), \n",
       "    ('pages', '519–528'), \n",
       "    ('numpages', '10'), \n",
       "    ('keywords', 'humor detection, product question answering'), \n",
       "    ('location', 'Virtual Event, China'), \n",
       "    ('series', \"SIGIR '20\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Ziser, Yftah'), Person('Kravi, Elad'), Person('Carmel, David')])]))), ('zhang2014recognize', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Recognizing Humor on Twitter'), \n",
       "    ('year', '2014'), \n",
       "    ('isbn', '9781450325981'), \n",
       "    ('publisher', 'Association for Computing Machinery'), \n",
       "    ('address', 'New York, NY, USA'), \n",
       "    ('url', 'https:\n",
       "    //doi.org/10.1145/2661829.2661997'), ('doi', '10.1145/2661829.2661997'), \n",
       "    ('abstract', 'In this paper, we present our work of humor recognition on Twitter, which will facilitate affect and sentimental analysis in the social network. The central question of what makes a tweet (Twitter post) humorous drives us to design humor-related features, which are derived from influential humor theories, linguistic norms, and affective dimensions. Using machine learning techniques, we are able to recognize humorous tweets with high accuracy and F-measure. More importantly, we single out features that contribute to distinguishing non-humorous tweets from humorous tweets, and humorous tweets from other short humorous texts (non-tweets). This proves that humorous tweets possess discernible characteristics that are neither found in plain tweets nor in humorous non-tweets. We believe our novel findings will inform and inspire the burgeoning field of computational humor research in the social media.'), \n",
       "    ('booktitle', 'Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management'), \n",
       "    ('pages', '889–898'), \n",
       "    ('numpages', '10'), \n",
       "    ('keywords', 'twitter, machine learning, humor recognition, computational humor'), \n",
       "    ('location', 'Shanghai, China'), \n",
       "    ('series', \"CIKM '14\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Zhang, Renxian'), Person('Liu, Naishi')])]))), ('weller-seppi-2019-humor', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Humor Detection: A Transformer Gets the Last Laugh'), \n",
       "    ('booktitle', 'Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)'), \n",
       "    ('month', 'November'), \n",
       "    ('year', '2019'), \n",
       "    ('address', 'Hong Kong, China'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/D19-1372'), \n",
       "    ('doi', '10.18653/v1/D19-1372'), \n",
       "    ('pages', '3621--3625'), \n",
       "    ('abstract', \"Much previous work has been done in attempting to identify humor in text. In this paper we extend that capability by proposing a new task: assessing whether or not a joke is humorous. We present a novel way of approaching this problem by building a model that learns to identify humorous jokes based on ratings gleaned from Reddit pages, consisting of almost 16,000 labeled instances. Using these ratings to determine the level of humor, we then employ a Transformer architecture for its advantages in learning from sentence context. We demonstrate the effectiveness of this approach and show results that are comparable to human performance. We further demonstrate our model{'}s increased capabilities on humor identification problems, such as the previously created datasets for short jokes and puns. These experiments show that this method outperforms all previous work done on these tasks, with an F-measure of 93.1{\\\\%} for the Puns dataset and 98.6{\\\\%} on the Short Jokes dataset.\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Weller, Orion'), Person('Seppi, Kevin')]), ('editor', [Person('Inui, Kentaro'), Person('Jiang, Jing'), Person('Ng, Vincent'), Person('Wan, Xiaojun')])]))), ('xie-etal-2021-uncertainty', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition'), \n",
       "    ('booktitle', 'Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)'), \n",
       "    ('month', 'August'), \n",
       "    ('year', '2021'), \n",
       "    ('address', 'Online'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2021.acl-short.6'), \n",
       "    ('doi', '10.18653/v1/2021.acl-short.6'), \n",
       "    ('pages', '33--39'), \n",
       "    ('abstract', 'Humor recognition has been widely studied as a text classification problem using data-driven approaches. However, most existing work does not examine the actual joke mechanism to understand humor. We break down any joke into two distinct components: the set-up and the punchline, and further explore the special relationship between them. Inspired by the incongruity theory of humor, we model the set-up as the part developing semantic uncertainty, and the punchline disrupting audience expectations. With increasingly powerful language models, we were able to feed the set-up along with the punchline into the GPT-2 language model, and calculate the uncertainty and surprisal values of the jokes. By conducting experiments on the SemEval 2021 Task 7 dataset, we found that these two features have better capabilities of telling jokes from non-jokes, compared with existing baselines.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Xie, Yubo'), Person('Li, Junze'), Person('Pu, Pearl')]), ('editor', [Person('Zong, Chengqing'), Person('Xia, Fei'), Person('Li, Wenjie'), Person('Navigli, Roberto')])]))), ('Peyrard2021LaughingHC', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Laughing Heads: Can Transformers Detect What Makes a Sentence Funny?'), \n",
       "    ('booktitle', 'International Joint Conference on Artificial Intelligence'), \n",
       "    ('year', '2021'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:234778118')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Peyrard, Maxime'), Person('Borges, Beatriz'), Person('Gligoric, Kristina'), Person('West, Robert')])]))), ('Annamoradnejad2020ColBERTUB', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'ColBERT: Using BERT sentence embedding in parallel neural networks for computational humor'), \n",
       "    ('journal', 'Expert Syst. Appl.'), \n",
       "    ('year', '2020'), \n",
       "    ('volume', '249'), \n",
       "    ('pages', '123685'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:254125774')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Annamoradnejad, Issa'), Person('Zoghi, Gohar')])]))), ('arora-etal-2022-transfer', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Transfer Learning for Humor Detection by Twin Masked Yellow {M}uppets'), \n",
       "    ('booktitle', 'Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)'), \n",
       "    ('month', 'November'), \n",
       "    ('year', '2022'), \n",
       "    ('address', 'Online only'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2022.aacl-short.1'), \n",
       "    ('pages', '1--7'), \n",
       "    ('abstract', 'Humorous texts can be of different forms such as punchlines, puns, or funny stories. Existing humor classification systems have been dealing with such diverse forms by treating them independently. In this paper, we argue that different forms of humor share a common background either in terms of vocabulary or constructs. As a consequence, it is likely that classification performance can be improved by jointly tackling different humor types. Hence, we design a shared-private multitask architecture following a transfer learning paradigm and perform experiments over four gold standard datasets. Empirical results steadily confirm our hypothesis by demonstrating statistically-significant improvements over baselines and accounting for new state-of-the-art figures for two datasets.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Arora, Aseem'), Person('Dias, Ga{\\\\\"e}l'), Person('Jatowt, Adam'), Person('Ekbal, Asif')]), ('editor', [Person('He, Yulan'), Person('Ji, Heng'), Person('Li, Sujian'), Person('Liu, Yang'), Person('Chang, Chua-Hui')])]))), ('potash-etal-2017-semeval', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{S}em{E}val-2017 Task 6: {\\\\#}{H}ashtag{W}ars: Learning a Sense of Humor'), \n",
       "    ('booktitle', 'Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)'), \n",
       "    ('month', 'August'), \n",
       "    ('year', '2017'), \n",
       "    ('address', 'Vancouver, Canada'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/S17-2004'), \n",
       "    ('doi', '10.18653/v1/S17-2004'), \n",
       "    ('pages', '49--57'), \n",
       "    ('abstract', \"This paper describes a new shared task for humor understanding that attempts to eschew the ubiquitous binary approach to humor detection and focus on comparative humor ranking instead. The task is based on a new dataset of funny tweets posted in response to shared hashtags, collected from the {`}Hashtag Wars{'} segment of the TV show @midnight. The results are evaluated in two subtasks that require the participants to generate either the correct pairwise comparisons of tweets (subtask A), or the correct ranking of the tweets (subtask B) in terms of how funny they are. 7 teams participated in subtask A, and 5 teams participated in subtask B. The best accuracy in subtask A was 0.675. The best (lowest) rank edit distance for subtask B was 0.872.\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Potash, Peter'), Person('Romanov, Alexey'), Person('Rumshisky, Anna')]), ('editor', [Person('Bethard, Steven'), Person('Carpuat, Marine'), Person('Apidianaki, Marianna'), Person('Mohammad, Saif M.'), Person('Cer, Daniel'), Person('Jurgens, David')])]))), ('van-hee-etal-2018-semeval', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{S}em{E}val-2018 Task 3: Irony Detection in {E}nglish Tweets'), \n",
       "    ('booktitle', 'Proceedings of the 12th International Workshop on Semantic Evaluation'), \n",
       "    ('month', 'June'), \n",
       "    ('year', '2018'), \n",
       "    ('address', 'New Orleans, Louisiana'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/S18-1005'), \n",
       "    ('doi', '10.18653/v1/S18-1005'), \n",
       "    ('pages', '39--50'), \n",
       "    ('abstract', 'This paper presents the first shared task on irony detection: given a tweet, automatic natural language processing systems should determine whether the tweet is ironic (Task A) and which type of irony (if any) is expressed (Task B). The ironic tweets were collected using irony-related hashtags (i.e. {\\\\#}irony, {\\\\#}sarcasm, {\\\\#}not) and were subsequently manually annotated to minimise the amount of noise in the corpus. Prior to distributing the data, hashtags that were used to collect the tweets were removed from the corpus. For both tasks, a training corpus of 3,834 tweets was provided, as well as a test set containing 784 tweets. Our shared tasks received submissions from 43 teams for the binary classification Task A and from 31 teams for the multiclass Task B. The highest classification scores obtained for both subtasks are respectively F1= 0.71 and F1= 0.51 and demonstrate that fine-grained irony classification is much more challenging than binary irony detection.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Van Hee, Cynthia'), Person('Lefever, Els'), Person(\"Hoste, V{\\\\'e}ronique\")]), ('editor', [Person('Apidianaki, Marianna'), Person('Mohammad, Saif M.'), Person('May, Jonathan'), Person('Shutova, Ekaterina'), Person('Bethard, Steven'), Person('Carpuat, Marine')])]))), ('Castro2018OverviewOT', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Overview of the HAHA Task: Humor Analysis Based on Human Annotation at IberEval 2018'), \n",
       "    ('booktitle', 'IberEval@SEPLN'), \n",
       "    ('year', '2018'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:51940157')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Castro, Santiago'), Person('Chiruzzo, Luis'), Person(\"Ros{\\\\'a}, Aiala\")])]))), ('hossain-etal-2020-semeval', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{S}em{E}val-2020 Task 7: Assessing Humor in Edited News Headlines'), \n",
       "    ('booktitle', 'Proceedings of the Fourteenth Workshop on Semantic Evaluation'), \n",
       "    ('month', 'December'), \n",
       "    ('year', '2020'), \n",
       "    ('address', 'Barcelona (online)'), \n",
       "    ('publisher', 'International Committee for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2020.semeval-1.98'), \n",
       "    ('doi', '10.18653/v1/2020.semeval-1.98'), \n",
       "    ('pages', '746--758'), \n",
       "    ('abstract', \"This paper describes the SemEval-2020 shared task {``}Assessing Humor in Edited News Headlines.{''} The task{'}s dataset contains news headlines in which short edits were applied to make them funny, and the funniness of these edited headlines was rated using crowdsourcing. This task includes two subtasks, the first of which is to estimate the funniness of headlines on a humor scale in the interval 0-3. The second subtask is to predict, for a pair of edited versions of the same original headline, which is the funnier version. To date, this task is the most popular shared computational humor task, attracting 48 teams for the first subtask and 31 teams for the second.\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Hossain, Nabil'), Person('Krumm, John'), Person('Gamon, Michael'), Person('Kautz, Henry')]), ('editor', [Person('Herbelot, Aurelie'), Person('Zhu, Xiaodan'), Person('Palmer, Alexis'), Person('Schneider, Nathan'), Person('May, Jonathan'), Person('Shutova, Ekaterina')])]))), ('radev-etal-2016-humor', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest'), \n",
       "    ('booktitle', \"Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)\"), \n",
       "    ('month', 'May'), \n",
       "    ('year', '2016'), \n",
       "    ('address', 'Portoro{\\\\v{z}}, Slovenia'), \n",
       "    ('publisher', 'European Language Resources Association (ELRA)'), \n",
       "    ('url', 'https://aclanthology.org/L16-1076'), \n",
       "    ('pages', '475--479'), \n",
       "    ('abstract', 'The New Yorker publishes a weekly captionless cartoon. More than 5,000 readers submit captions for it. The editors select three of them and ask the readers to pick the funniest one. We describe an experiment that compares a dozen automatic methods for selecting the funniest caption. We show that negative sentiment, human-centeredness, and lexical centrality most strongly match the funniest captions, followed by positive sentiment. These results are useful for understanding humor and also in the design of more engaging conversational agents in text and multimodal (vision+text) systems. As part of this work, a large set of cartoons and captions is being made available to the community.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Radev, Dragomir'), Person('Stent, Amanda'), Person('Tetreault, Joel'), Person('Pappu, Aasish'), Person('Iliakopoulou, Aikaterini'), Person('Chanfreau, Agustin'), Person('de Juan, Paloma'), Person('Vallmitjana, Jordi'), Person('Jaimes, Alejandro'), Person('Jha, Rahul'), Person('Mankoff, Robert')]), ('editor', [Person('Calzolari, Nicoletta'), Person('Choukri, Khalid'), Person('Declerck, Thierry'), Person('Goggi, Sara'), Person('Grobelnik, Marko'), Person('Maegaard, Bente'), Person('Mariani, Joseph'), Person('Mazo, Helene'), Person('Moreno, Asuncion'), Person('Odijk, Jan'), Person('Piperidis, Stelios')])]))), ('Shahaf2015InsideJI', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Inside Jokes: Identifying Humorous Cartoon Captions'), \n",
       "    ('journal', 'Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining'), \n",
       "    ('year', '2015'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:14570747')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Shahaf, Dafna'), Person('Horvitz, Eric'), Person('Mankoff, Robert')])]))), ('hasan-etal-2019-ur', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{UR}-{FUNNY}: A Multimodal Language Dataset for Understanding Humor'), \n",
       "    ('booktitle', 'Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)'), \n",
       "    ('month', 'November'), \n",
       "    ('year', '2019'), \n",
       "    ('address', 'Hong Kong, China'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/D19-1211'), \n",
       "    ('doi', '10.18653/v1/D19-1211'), \n",
       "    ('pages', '2046--2056'), \n",
       "    ('abstract', 'Humor is a unique and creative communicative behavior often displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (visual) and prosodic cues (acoustic). Understanding humor from these three modalities falls within boundaries of multimodal language; a recent research trend in natural language processing that models natural language as it happens in face-to-face communication. Although humor detection is an established research area in NLP, in a multimodal context it has been understudied. This paper presents a diverse multimodal dataset, called UR-FUNNY, to open the door to understanding multimodal language used in expressing humor. The dataset and accompanying studies, present a framework in multimodal humor detection for the natural language processing community. UR-FUNNY is publicly available for research.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Hasan, Md Kamrul'), Person('Rahman, Wasifur'), Person('Bagher Zadeh, AmirAli'), Person('Zhong, Jianyuan'), Person('Tanveer, Md Iftekhar'), Person('Morency, Louis-Philippe'), Person('Hoque, Mohammed (Ehsan)')]), ('editor', [Person('Inui, Kentaro'), Person('Jiang, Jing'), Person('Ng, Vincent'), Person('Wan, Xiaojun')])]))), ('bertero-fung-2016-deep', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Deep Learning of Audio and Language Features for Humor Prediction'), \n",
       "    ('booktitle', \"Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)\"), \n",
       "    ('month', 'May'), \n",
       "    ('year', '2016'), \n",
       "    ('address', 'Portoro{\\\\v{z}}, Slovenia'), \n",
       "    ('publisher', 'European Language Resources Association (ELRA)'), \n",
       "    ('url', 'https://aclanthology.org/L16-1079'), \n",
       "    ('pages', '496--501'), \n",
       "    ('abstract', \"We propose a comparison between various supervised machine learning methods to predict and detect humor in dialogues. We retrieve our humorous dialogues from a very popular TV sitcom: {``}The Big Bang Theory{''}. We build a corpus where punchlines are annotated using the canned laughter embedded in the audio track. Our comparative study involves a linear-chain Conditional Random Field over a Recurrent Neural Network and a Convolutional Neural Network. Using a combination of word-level and audio frame-level features, the CNN outperforms the other methods, obtaining the best F-score of 68.5{\\\\%} over 66.5{\\\\%} by CRF and 52.9{\\\\%} by RNN. Our work is a starting point to developing more effective machine learning and neural network models on the humor prediction task, as well as developing machines capable in understanding humor in general.\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Bertero, Dario'), Person('Fung, Pascale')]), ('editor', [Person('Calzolari, Nicoletta'), Person('Choukri, Khalid'), Person('Declerck, Thierry'), Person('Goggi, Sara'), Person('Grobelnik, Marko'), Person('Maegaard, Bente'), Person('Mariani, Joseph'), Person('Mazo, Helene'), Person('Moreno, Asuncion'), Person('Odijk, Jan'), Person('Piperidis, Stelios')])]))), ('buscaldi2007some', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Some experiments in humour recognition using the italian wikiquote collection'), \n",
       "    ('booktitle', 'International workshop on fuzzy logic and applications'), \n",
       "    ('pages', '464--468'), \n",
       "    ('year', '2007'), \n",
       "    ('organization', 'Springer')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Buscaldi, Davide'), Person('Rosso, Paolo')])]))), ('castro2017crowd', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'A crowd-annotated spanish corpus for humor analysis'), \n",
       "    ('journal', 'arXiv preprint arXiv:1710.00477'), \n",
       "    ('year', '2017')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Castro, Santiago'), Person('Chiruzzo, Luis'), Person(\"Ros{\\\\'a}, Aiala\"), Person('Garat, Diego'), Person('Moncecchi, Guillermo')])]))), ('winters2020dutch', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Dutch humor detection by generating negative examples'), \n",
       "    ('journal', 'arXiv preprint arXiv:2010.13652'), \n",
       "    ('year', '2020')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Winters, Thomas'), Person('Delobelle, Pieter')])]))), ('blinov2019large', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Large dataset and language model fun-tuning for humor recognition'), \n",
       "    ('booktitle', 'Proceedings of the 57th annual meeting of the association for computational linguistics'), \n",
       "    ('pages', '4027--4032'), \n",
       "    ('year', '2019')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Blinov, Vladislav'), Person('Bolotova-Baranova, Valeria'), Person('Braslavski, Pavel')])]))), ('kocon2023chatgpt', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'ChatGPT: Jack of all trades, master of none'), \n",
       "    ('journal', 'Information Fusion'), \n",
       "    ('volume', '99'), \n",
       "    ('pages', '101861'), \n",
       "    ('year', '2023'), \n",
       "    ('publisher', 'Elsevier')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person(\"Koco{\\\\'n}, Jan\"), Person('Cichecki, Igor'), Person('Kaszyca, Oliwier'), Person('Kochanek, Mateusz'), Person('Szyd{\\\\l}o, Dominika'), Person('Baran, Joanna'), Person('Bielaniewicz, Julita'), Person('Gruza, Marcin'), Person('Janz, Arkadiusz'), Person('Kanclerz, Kamil'), Person('others')])]))), ('borji2023categoricalarchivechatgptfailures', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'A Categorical Archive of ChatGPT Failures'), \n",
       "    ('year', '2023'), \n",
       "    ('eprint', '2302.03494'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL'), \n",
       "    ('url', 'https://arxiv.org/abs/2302.03494')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Borji, Ali')])]))), ('baranov-etal-2023-told', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'You Told Me That Joke Twice: A Systematic Investigation of Transferability and Robustness of Humor Detection Models'), \n",
       "    ('booktitle', 'Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing'), \n",
       "    ('month', 'December'), \n",
       "    ('year', '2023'), \n",
       "    ('address', 'Singapore'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2023.emnlp-main.845'), \n",
       "    ('doi', '10.18653/v1/2023.emnlp-main.845'), \n",
       "    ('pages', '13701--13715'), \n",
       "    ('abstract', 'In this study, we focus on automatic humor detection, a highly relevant task for conversational AI. To date, there are several English datasets for this task, but little research on how models trained on them generalize and behave in the wild. To fill this gap, we carefully analyze existing datasets, train RoBERTa-based and Na{\\\\\"\\\\i}ve Bayes classifiers on each of them, and test on the rest. Training and testing on the same dataset yields good results, but the transferability of the models varies widely. Models trained on datasets with jokes from different sources show better transferability, while the amount of training data has a smaller impact. The behavior of the models on out-of-domain data is unstable, suggesting that some of the models overfit, while others learn non-specific humor characteristics. An adversarial attack shows that models trained on pun datasets are less robust. We also evaluate the sense of humor of the chatGPT and Flan-UL2 models in a zero-shot scenario. The LLMs demonstrate competitive results on humor datasets and a more stable behavior on out-of-domain data. We believe that the obtained results will facilitate the development of new datasets and evaluation methodologies in the field of computational humor. We{\\'}ve made all the data from the study and the trained models publicly available at https://github.com/Humor-Research/Humor-detection.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Baranov, Alexander'), Person('Kniazhevsky, Vladimir'), Person('Braslavski, Pavel')]), ('editor', [Person('Bouamor, Houda'), Person('Pino, Juan'), Person('Bali, Kalika')])]))), ('mihalcea-strapparava-2005-making', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Making Computers Laugh: Investigations in Automatic Humor Recognition'), \n",
       "    ('booktitle', 'Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing'), \n",
       "    ('month', 'October'), \n",
       "    ('year', '2005'), \n",
       "    ('address', 'Vancouver, British Columbia, Canada'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/H05-1067'), \n",
       "    ('pages', '531--538')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Mihalcea, Rada'), Person('Strapparava, Carlo')]), ('editor', [Person('Mooney, Raymond'), Person('Brew, Chris'), Person('Chien, Lee-Feng'), Person('Kirchhoff, Katrin')])]))), ('tang2022naughtyformertransformerunderstandsoffensive', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'The Naughtyformer: A Transformer Understands Offensive Humor'), \n",
       "    ('year', '2022'), \n",
       "    ('eprint', '2211.14369'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL'), \n",
       "    ('url', 'https://arxiv.org/abs/2211.14369')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Tang, Leonard'), Person('Cai, Alexander'), Person('Li, Steve'), Person('Wang, Jason')])]))), ('hossain-etal-2020-stimulating', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Stimulating Creativity with {F}un{L}ines: A Case Study of Humor Generation in Headlines'), \n",
       "    ('booktitle', 'Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations'), \n",
       "    ('month', 'July'), \n",
       "    ('year', '2020'), \n",
       "    ('address', 'Online'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2020.acl-demos.28'), \n",
       "    ('doi', '10.18653/v1/2020.acl-demos.28'), \n",
       "    ('pages', '256--262'), \n",
       "    ('abstract', 'Building datasets of creative text, such as humor, is quite challenging. We introduce FunLines, a competitive game where players edit news headlines to make them funny, and where they rate the funniness of headlines edited by others. FunLines makes the humor generation process fun, interactive, collaborative, rewarding and educational, keeping players engaged and providing humor data at a very low cost compared to traditional crowdsourcing approaches. FunLines offers useful performance feedback, assisting players in getting better over time at generating and assessing humor, as our analysis shows. This helps to further increase the quality of the generated dataset. We show the effectiveness of this data by training humor classification models that outperform a previous benchmark, and we release this dataset to the public.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Hossain, Nabil'), Person('Krumm, John'), Person('Sajed, Tanvir'), Person('Kautz, Henry')]), ('editor', [Person('Celikyilmaz, Asli'), Person('Wen, Tsung-Hsien')])]))), ('Xie2023FunQATS', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'FunQA: Towards Surprising Video Comprehension'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2023'), \n",
       "    ('volume', 'abs/2306.14899'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:259262297')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Xie, Binzhu'), Person('Zhang, Sicheng'), Person('Zhou, Zitang'), Person('Li, Bo'), Person('Zhang, Yuanhan'), Person('Hessel, Jack'), Person('Yang, Jingkang'), Person('Liu, Ziwei')])]))), ('Zhong2023LetsTO', Entry('article',\n",
       "  fields=[\n",
       "    ('title', \"Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation\"), \n",
       "    ('journal', '2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)'), \n",
       "    ('year', '2023'), \n",
       "    ('pages', '13246-13257'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:265659076')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Zhong, Shan'), Person('Huang, Zhongzhan'), Person('Gao, Shanghua'), Person('Wen, Wushao'), Person('Lin, Liang'), Person('Zitnik, Marinka'), Person('Zhou, Pan')])]))), ('GesIsGG2023', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Is GPT-4 Good Enough to Evaluate Jokes?'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:265255804'), \n",
       "    ('year', '2023')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person(\"G{\\\\'o}es, Fabr{\\\\'i}cio\"), Person('Sawicki, Piotr'), Person('Grze´s, Marek'), Person('Brown, Daniel'), Person('Volpe, Marco')])]))), ('Swanson2021StoryCL', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Story Centaur: Large Language Model Few Shot Learning as a Creative Writing Tool'), \n",
       "    ('booktitle', 'Conference of the European Chapter of the Association for Computational Linguistics'), \n",
       "    ('year', '2021'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:233365238')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Swanson, Benjamin'), Person('Mathewson, Kory Wallace'), Person('Pietrzak, Ben'), Person('Chen, Sherol'), Person('Dinalescu, Monica')])]))), ('Maher2010EvaluatingCI', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Evaluating creativity in humans, computers, and collectively intelligent systems'), \n",
       "    ('booktitle', 'Network Conference on Creativity and Innovation in Design'), \n",
       "    ('year', '2010'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:4840039')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Maher, Mary Lou')])]))), ('Gunkle1975AestheticsAP', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Aesthetics and Psychobiology'), \n",
       "    ('year', '1975'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:144314694')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Gunkle, George'), Person('Berlyne, Daniel E.')])]))), ('Elgammal2015QuantifyingCI', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Quantifying Creativity in Art Networks'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2015'), \n",
       "    ('volume', 'abs/1506.00711'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:14607648')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Elgammal, A.'), Person('Saleh, Babak')])]))), ('Gatt2017SurveyOT', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2017'), \n",
       "    ('volume', 'abs/1703.09902'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:16946362')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Gatt, Albert'), Person('Krahmer, Emiel J.')])]))), ('zhao-etal-2023-discoscore', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{D}isco{S}core: Evaluating Text Generation with {BERT} and Discourse Coherence'), \n",
       "    ('booktitle', 'Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics'), \n",
       "    ('month', 'May'), \n",
       "    ('year', '2023'), \n",
       "    ('address', 'Dubrovnik, Croatia'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2023.eacl-main.278'), \n",
       "    ('doi', '10.18653/v1/2023.eacl-main.278'), \n",
       "    ('pages', '3865--3883')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Zhao, Wei'), Person('Strube, Michael'), Person('Eger, Steffen')]), ('editor', [Person('Vlachos, Andreas'), Person('Augenstein, Isabelle')])]))), ('Laban2021CanTM', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Can Transformer Models Measure Coherence In Text: Re-Thinking the Shuffle Test'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2021'), \n",
       "    ('volume', 'abs/2107.03448'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:235656966')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Laban, Philippe'), Person('Dai, Luke'), Person('Bandarkar, Lucas')])]))), ('wisniewski1997concepts', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'When concepts combine'), \n",
       "    ('journal', 'Psychonomic bulletin \\\\& review'), \n",
       "    ('volume', '4'), \n",
       "    ('pages', '167--183'), \n",
       "    ('year', '1997'), \n",
       "    ('publisher', 'Springer')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Wisniewski, Edward J')])]))), ('costello2000efficient', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Efficient creativity: Constraint-guided conceptual combination'), \n",
       "    ('journal', 'Cognitive Science'), \n",
       "    ('volume', '24'), \n",
       "    ('number', '2'), \n",
       "    ('pages', '299--349'), \n",
       "    ('year', '2000'), \n",
       "    ('publisher', 'Wiley Online Library')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Costello, Fintan J'), Person('Keane, Mark T')])]))), ('ismayilzada2024evaluatingmorphologicalcompositionalgeneralization', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Evaluating Morphological Compositional Generalization in Large Language Models'), \n",
       "    ('year', '2024'), \n",
       "    ('eprint', '2410.12656'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL'), \n",
       "    ('url', 'https://arxiv.org/abs/2410.12656')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Ismayilzada, Mete'), Person('Circi, Defne'), Person('Sälevä, Jonne'), Person('Sirin, Hale'), Person('Köksal, Abdullatif'), Person('Dhingra, Bhuwan'), Person('Bosselut, Antoine'), Person('van der Plas, Lonneke'), Person('Ataman, Duygu')])]))), ('anh-etal-2024-morphology', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Morphology Matters: Probing the Cross-linguistic Morphological Generalization Abilities of Large Language Models through a Wug Test'), \n",
       "    ('booktitle', 'Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics'), \n",
       "    ('month', 'August'), \n",
       "    ('year', '2024'), \n",
       "    ('address', 'Bangkok, Thailand'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2024.cmcl-1.15'), \n",
       "    ('doi', '10.18653/v1/2024.cmcl-1.15'), \n",
       "    ('pages', '177--188'), \n",
       "    ('abstract', \"We develop a multilingual version of the Wug Test, an artificial word completion experiment that is typically used to test the morphological knowledge of children, and apply it to the GPT family of large language models (LLMs). LLMs{'} performance on this test was evaluated by native speakers of six different languages, who judged whether the inflected and derived forms generated by the models conform to the morphological rules of their language. Our results show that LLMs can generalize their morphological knowledge to new, unfamiliar words, but that their success in generating the {``}correct{''} generalization (as judged by native human speakers) is predicted by a language{'}s morphological complexity (specifically, integrative complexity). We further find that the amount of training data has surprisingly little on LLMs{'} morphological generalization abilities within the scope of the analyzed languages. These findings highlight that {``}morphology matters{''}, and have important implications for improving low-resource language modeling.\")],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Anh, Dang'), Person('Raviv, Limor'), Person('Galke, Lukas')]), ('editor', [Person('Kuribayashi, Tatsuki'), Person('Rambelli, Giulia'), Person('Takmaz, Ece'), Person('Wicke, Philipp'), Person('Oseki, Yohei')])]))), ('lu2024aihumanityssalieriquantifying', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', \"AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text\"), \n",
       "    ('year', '2024'), \n",
       "    ('eprint', '2410.04265'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.CL'), \n",
       "    ('url', 'https://arxiv.org/abs/2410.04265')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Lu, Ximing'), Person('Sclar, Melanie'), Person('Hallinan, Skyler'), Person('Mireshghallah, Niloofar'), Person('Liu, Jiacheng'), Person('Han, Seungju'), Person('Ettinger, Allyson'), Person('Jiang, Liwei'), Person('Chandu, Khyathi'), Person('Dziri, Nouha'), Person('Choi, Yejin')])]))), ('demis-creativity2018', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Creativity and AI'), \n",
       "    ('year', '2018'), \n",
       "    ('publisher', 'The Rothschild Foundation Lecture - Royal Academy of Arts'), \n",
       "    ('url', 'https://www.youtube.com/watch?v=d-bvsJWmqlc&ab_channel=RoyalAcademyofArts')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Hassabis, Demis')])]))), ('Frana2016RegentDependentCA', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Regent-Dependent Creativity: A Domain Independent Metric for the Assessment of Creative Artifacts'), \n",
       "    ('booktitle', 'International Conference on Innovative Computing and Cloud Computing'), \n",
       "    ('year', '2016'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:7817742')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('França, Celso'), Person(\"G{\\\\'o}es, Lu{\\\\'i}s Fabr{\\\\'i}cio Wanderley\"), Person('Amorim, Alvaro'), Person('Rocha, Rodrigo C. O.'), Person('Silva, Alysson Ribeiro Da')])]))), ('Lamb2015HumanCI', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Human Competence in Creativity Evaluation'), \n",
       "    ('booktitle', 'International Conference on Innovative Computing and Cloud Computing'), \n",
       "    ('year', '2015'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:14806090')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Lamb, Carolyn'), Person('Brown, Daniel G.'), Person('Clarke, Charles L. A.')])]))), ('cook2015generating', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Generating Code For Expressing Simple Preferences: Moving On From Hardcoding And Randomness.'), \n",
       "    ('booktitle', 'ICCC'), \n",
       "    ('pages', '8--16'), \n",
       "    ('year', '2015')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Cook, Michael'), Person('Colton, Simon')])]))), ('jordanous2015measuring', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Measuring cultural value using social network analysis: a case study on valuing electronic musicians'), \n",
       "    ('year', '2015'), \n",
       "    ('publisher', 'Citeseer')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Jordanous, Anna'), Person('Allington, Daniel'), Person('Dueck, Byron')])]))), ('maher2012using', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', 'Using AI to evaluate creative designs'), \n",
       "    ('booktitle', 'DS 73-1 Proceedings of the 2nd International Conference on Design Creativity Volume 1'), \n",
       "    ('year', '2012')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Maher, Mary Lou'), Person('Fisher, Douglas H')])]))), ('saphra2024mechanistic', Entry('misc',\n",
       "  fields=[\n",
       "    ('title', 'Mechanistic?'), \n",
       "    ('year', '2024'), \n",
       "    ('eprint', '2410.09087'), \n",
       "    ('archivePrefix', 'arXiv'), \n",
       "    ('primaryClass', 'cs.AI'), \n",
       "    ('url', 'https://arxiv.org/abs/2410.09087')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Saphra, Naomi'), Person('Wiegreffe, Sarah')])]))), ('Bereska2024MechanisticIF', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Mechanistic Interpretability for AI Safety - A Review'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2024'), \n",
       "    ('volume', 'abs/2404.14082'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:269293418')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Bereska, Leonard'), Person('Gavves, Efstratios')])]))), ('Marco2024PronVP', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Pron vs Prompt: Can Large Language Models already Challenge a World-Class Fiction Author at Creative Text Writing?'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2024'), \n",
       "    ('volume', 'abs/2407.01119'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:270870769')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Marco, Guillermo'), Person('Gonzalo, Julio'), Person(\"del Castillo, Ram'on\"), Person(\"Girona, Mar'ia Teresa Mateo\")])]))), ('Lu2024BenchmarkingLM', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Benchmarking Language Model Creativity: A Case Study on Code Generation'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2024'), \n",
       "    ('volume', 'abs/2407.09007'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:271162279')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Lu, Yining'), Person('Wang, Dixuan'), Person('Li, Tianjian'), Person('Jiang, Dongwei'), Person('Khashabi, Daniel')])]))), ('Mehrotra2024EnhancingCI', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Enhancing Creativity in Large Language Models through Associative Thinking Strategies'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2024'), \n",
       "    ('volume', 'abs/2405.06715'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:269756853')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Mehrotra, Pronita'), Person('Parab, Aishni'), Person('Gulwani, Sumit')])]))), ('Tian2024AreLL', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Are Large Language Models Capable of Generating Human-Level Narratives?'), \n",
       "    ('journal', 'ArXiv'), \n",
       "    ('year', '2024'), \n",
       "    ('volume', 'abs/2407.13248'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:271270813')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Tian, Yufei'), Person('Huang, Tenghao'), Person('Liu, Miri'), Person('Jiang, Derek'), Person('Spangher, Alexander'), Person('Chen, Muhao'), Person('May, Jonathan'), Person('Peng, Nanyun')])]))), ('Chhun2024DoLM', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Do Language Models Enjoy Their Own Stories? Prompting Large Language Models for Automatic Story Evaluation'), \n",
       "    ('journal', 'Transactions of the Association for Computational Linguistics'), \n",
       "    ('year', '2024'), \n",
       "    ('volume', '12'), \n",
       "    ('pages', '1122-1142'), \n",
       "    ('url', 'https://api.semanticscholar.org/CorpusID:269982125')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Chhun, Cyril'), Person('Suchanek, Fabian M.'), Person(\"Clavel, Chlo{\\\\'e}\")])]))), ('akoury-etal-2020-storium', Entry('inproceedings',\n",
       "  fields=[\n",
       "    ('title', '{STORIUM}: {A} {D}ataset and {E}valuation {P}latform for {M}achine-in-the-{L}oop {S}tory {G}eneration'), \n",
       "    ('booktitle', 'Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)'), \n",
       "    ('month', 'November'), \n",
       "    ('year', '2020'), \n",
       "    ('address', 'Online'), \n",
       "    ('publisher', 'Association for Computational Linguistics'), \n",
       "    ('url', 'https://aclanthology.org/2020.emnlp-main.525'), \n",
       "    ('doi', '10.18653/v1/2020.emnlp-main.525'), \n",
       "    ('pages', '6470--6484'), \n",
       "    ('abstract', 'Systems for story generation are asked to produce plausible and enjoyable stories given an input context. This task is underspecified, as a vast number of diverse stories can originate from a single input. The large output space makes it difficult to build and evaluate story generation models, as (1) existing datasets lack rich enough contexts to meaningfully guide models, and (2) existing evaluations (both crowdsourced and automatic) are unreliable for assessing long-form creative text. To address these issues, we introduce a dataset and evaluation platform built from STORIUM, an online collaborative storytelling community. Our author-generated dataset contains 6K lengthy stories (125M tokens) with fine-grained natural language annotations (e.g., character goals and attributes) interspersed throughout each narrative, forming a robust source for guiding models. We evaluate language models fine-tuned on our dataset by integrating them onto STORIUM, where real authors can query a model for suggested story continuations and then edit them. Automatic metrics computed over these edits correlate well with both user ratings of generated stories and qualitative feedback from semi-structured user interviews. We release both the STORIUM dataset and evaluation platform to spur more principled research into story generation.')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Akoury, Nader'), Person('Wang, Shufan'), Person('Whiting, Josh'), Person('Hood, Stephen'), Person('Peng, Nanyun'), Person('Iyyer, Mohit')]), ('editor', [Person('Webber, Bonnie'), Person('Cohn, Trevor'), Person('He, Yulan'), Person('Liu, Yang')])]))), ('yuan2024chatmusician', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Chatmusician: Understanding and generating music intrinsically with llm'), \n",
       "    ('journal', 'arXiv preprint arXiv:2402.16153'), \n",
       "    ('year', '2024')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Yuan, Ruibin'), Person('Lin, Hanfeng'), Person('Wang, Yi'), Person('Tian, Zeyue'), Person('Wu, Shangda'), Person('Shen, Tianhao'), Person('Zhang, Ge'), Person('Wu, Yuhang'), Person('Liu, Cong'), Person('Zhou, Ziya'), Person('others')])]))), ('deng2024composerx', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'ComposerX: Multi-Agent Symbolic Music Composition with LLMs'), \n",
       "    ('journal', 'arXiv preprint arXiv:2404.18081'), \n",
       "    ('year', '2024')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Deng, Qixin'), Person('Yang, Qikai'), Person('Yuan, Ruibin'), Person('Huang, Yipeng'), Person('Wang, Yi'), Person('Liu, Xubo'), Person('Tian, Zeyue'), Person('Pan, Jiahao'), Person('Zhang, Ge'), Person('Lin, Hanfeng'), Person('others')])]))), ('qu2024mupt', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'Mupt: A generative symbolic music pretrained transformer'), \n",
       "    ('journal', 'arXiv preprint arXiv:2404.06393'), \n",
       "    ('year', '2024')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Qu, Xingwei'), Person('Bai, Yuelin'), Person('Ma, Yinghao'), Person('Zhou, Ziya'), Person('Lo, Ka Man'), Person('Liu, Jiaheng'), Person('Yuan, Ruibin'), Person('Min, Lejun'), Person('Liu, Xueling'), Person('Zhang, Tianyu'), Person('others')])]))), ('peeperkorn2023characterizations', Entry('article',\n",
       "  fields=[\n",
       "    ('title', 'On characterizations of large language models and creativity evaluation'), \n",
       "    ('year', '2023'), \n",
       "    ('publisher', 'Association for Computational Creativity')],\n",
       "  persons=OrderedCaseInsensitiveDict([('author', [Person('Peeperkorn, Max'), Person('Brown, Dan'), Person('Jordanous, Anna')])])))])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bib_data.entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Lovelace, Lady'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entry = list(bib_data.entries.items())[0]\n",
    "person = list(entry[1].persons.items())[0][1]\n",
    "str(person[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "entry_template = \"\"\"\n",
    "1. **[{title}]({url})**\n",
    "\n",
    "    *{authors}*\n",
    "\n",
    "    <u>{journal}</u> {year}\n",
    "\"\"\"\n",
    "\n",
    "with open('assets/literature.md', 'w') as f:\n",
    "    for entry_key, entry in bib_data.entries.items():\n",
    "        title = entry.fields['title']\n",
    "        authors = [\" \".join(person.first_names + person.middle_names + person.last_names) for person in entry.persons[\"author\"]]\n",
    "        journal = entry.fields.get('journal', \"\")\n",
    "        year = entry.fields.get('year')\n",
    "        url = entry.fields.get('url')\n",
    "        publisher = entry.fields.get('publisher', \"\")\n",
    "        booktitle = entry.fields.get('booktitle', \"\")\n",
    "\n",
    "        journal = f\"{booktitle}. {journal}. {publisher}\".replace(\". .\", \".\").replace(\"..\", \".\").strip(\".\").strip().strip(\".\")\n",
    "\n",
    "        if journal:\n",
    "            journal = f\"{journal}.\"\n",
    "\n",
    "        f.write(entry_template.format(\n",
    "            title=title,\n",
    "            authors=\", \".join(authors),\n",
    "            journal=journal,\n",
    "            year=year,\n",
    "            url=url\n",
    "        ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pybtex.database import parse_file\n",
    "\n",
    "bib_data = parse_file('temp.bib')\n",
    "\n",
    "# remove url field\n",
    "\n",
    "for entry_key, entry in bib_data.entries.items():\n",
    "    if 'url' in entry.fields:\n",
    "        del entry.fields['url']\n",
    "    if \"doi\" in entry.fields:\n",
    "        del entry.fields['doi']\n",
    "    if \"eprint\" in entry.fields:\n",
    "        del entry.fields['eprint']\n",
    "\n",
    "bib_data.to_file('temp2.bib')"
   ]
  }
 ],
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