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<!DOCTYPE html>
<html>
<head>
<title>CogBench</title>
<link rel="icon" type="image/x-icon" href="static/images/favicon.ico" />
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href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
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<meta name="viewport" content="width=device-width, initial-scale=1">
</head>
<body>
<!-- <section class="hero is-warning">
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<p class="title">
Under Construction
</p>
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Internal preview only
</p>
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<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-2 publication-title">
CogBench
</h1>
<h1 class="subtitle is-2 publication-title">
Thinking like a human, thinking like a machine:<br/> What's the difference?
</h1>
<div class="is-size-5 publication-authors">
<div class="is-size-5 publication-authors">
<a href="https://minnesotanlp.github.io/">University of Minnesota</a>,
<a href="">University of Wisconsin</a>,
<a href="">Hamline University</a>
<span class="eql-cntrb"
>
<!--
<small><br /><sup>*</sup>Main Developers <sup>+</sup>Contributors</small
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<a
href="https://docs.google.com/forms/d/e/1FAIpQLSdAyv3ELW09sRa1L_xQrDXS9q1aeWtntdjAFpeH4SXJoe6FVQ/viewform"
target="_blank"
class="external-link button is-normal is-rounded is-dark is-outlined"
>
<span>Collaboration Interest Form</span>
</a>
</span>
<!-- Github link -->
<span class="link-block">
<a
href="https://cogbench-prd.ngrok.dev"
target="_blank"
class="external-link button is-normal is-rounded is-dark is-outlined"
>
<span>Data Collection Tool</span>
</a>
</span>
<!-- Github link -->
<span class="link-block">
<a
href="static/pdfs/instructions.pdf"
target="_blank"
class="external-link button is-normal is-rounded is-dark is-outlined"
>
<span>Instructions for Contributors</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="section hero is-small left-aligned is-light" style="text-align: left;">
<div class="container is-max-desktop" style="text-align: left;">
<div class="content" style="text-align: left;">
<h3>CogBench is a project bridging machine learning and human-centered disciplines.
<br/><br/>We aim to broadly study cognition across both language models and humans.</h3>
</div>
</div>
</section>
<!-- Research question start -->
<section class="section hero is-small left-aligned" style="text-align: left;">
<div class="container is-max-desktop" style="text-align: left;">
<div class="content" style="text-align: left;">
<h2 class="title is-3"> Members</h2>
We are an interdisciplinary group studying cognition from various research fields, such as Psychology, English, Educational Psychology, and Computer Science.
<br><br>
<p>
<b>Student Members:</b>
<a href="https://karinjd.github.io/" class="link">Karin de Langis*</a>,
<a >Jong Inn Park</a>,
<a >Bin Hu</a>,
<a >Stuti Shah</a>
</p>
<p> <b>Contributors: </b>
<a >Andreas Schramm</a> (Lingusitics),
<a href="https://cla.umn.edu/about/directory/profile/elfen001" >Andrew Elfenbein</a> (English),
<a href="https://sites.google.com/site/michaelmensink/">Mike Mensink</a> (Psychology)
</p>
<p> <b>Principal Investigator: </b><a href="https://dykang.github.io/" >Dongyeop Kang</a> </p>
</div>
</section>
<!-- Research question end -->
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<h2 class="title is-3 ">Objective</h2>
<p>The project’s goals include:
<ol>
<li>Identify and formalize cognitive and linguistic distinctions between language models and humans</li>
<li> Test theories/models/frameworks developed in human studies within an artificial intelligence setting
</li>
<li> Build foundation for understanding 'internals' of LLM cognition - more predictive power, interpretability for LLM behavior
</li>
<li>Explore feasibility of LLM assistance in materials development and validation for human studies</li>
</ol>
<br/><br/>
</p>
<!--<p>The state of the art in LLMs has advanced rapidly, demanding new suites of benchmarks to assess emergent capabilities. For example, contemporary work explores benchmarking LLMs' Emotional Intelligence (EQ)
and decision making (CogBench). We want to develop a broad spectrum cognitive benchmark that evaluates LLMs across areas of cognition that are well-studied in humans - e.g. selective attention, working memory,
psycholinguistics. Our aim is to understand the ways in which LLM cognition differs from humans, as well as the ways in which it is similar.
</p>
<p><b> Project Goals: </b></p>
<ol>
<li> Create a detailed cognitive inventory to benchmark LLM capabilities</li>
<li> Utilize domain experts to probe cognition in LLMs: Cognitive Science, Psychology, Linguistics
</li>
<li>Facilitate deeper understanding of LLM cognition compared to human cognition</li>
<li> Build foundation for understanding 'internals' of LLM cognition - more predictive power, interpretability for LLM behavior
</li>
</ol>-->
</div>
</div>
</div>
</div>
</ul>
</div>
</div>
</section>
</span>
<!-- Takeaway end -->
<!-- Research question start -->
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<div class="container is-max-desktop">
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<h2 class="title is-3">Join our project!</h2>
<div class="level-set has-text-justified">
We are currently expanding our team to include more researchers in psychology, linguistics, cognitive science, and related fields. If you are interested in collaborating and co-authoring with us, please be in touch.
<p> The best way to contact us is by filling out the <a href="https://docs.google.com/forms/d/e/1FAIpQLSdAyv3ELW09sRa1L_xQrDXS9q1aeWtntdjAFpeH4SXJoe6FVQ/viewform" target="_blank" class="external-link is-normal"><span> <u>collaboration interest form</u></span></a>. </p>
<p> We are looking for collaborators willing to contribute data, provide feedback on our data annotation tool, and/or co-author our paper. </p>
</div>
</div>
</section>
<!-- Research question end -->
<!-- Data type start -->
<section class="section hero is-small is-light">
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<h2 class="title is-3 ">Demo Video</h2>
<video poster="" id="tree" autoplay controls muted loop height="100%">
<!-- Your video here -->
<source src="static/videos/cogbench-demo-final.mov"
type="video/mp4">
</video>
<p class="subtitle has-text-centered">
View this video for a demo of the data collection web interface. We will provide the login credentials once you fill out our interest form!
</p>
<p><a href="static/videos/cogbench-demo-final.mov" target="_blank" class="external-link is-normal"><span> <b>Link to Video</b></span></a> </p>
</div>
</div>
</div>
</section>
<!-- Data type end -->
<!-- Research Contributions start
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<h2 class="title is-3">Example Output Data</h2>
<div class="level-set has-text-justified">
<img src="static/images/Prompting.png" style="margin-inline: 5px;">
</div>
</div>
</div>
</section>
Research Contributions end -->
<!-- Image carousel -->
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<h2 class="title is-3">Example Output Data</h2>
<div id="results-carousel" class="carousel results-carousel">
<div class="item">
<!-- Your image here -->
<img src="static/images/prompt_1.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
</h2>
</div>
<div class="item">
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<img src="static/images/prompt_2.png" alt="MY ALT TEXT"/>
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<img src="static/images/prompt_3.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
</h2>
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</div>
</section>
<!-- End image carousel -->
<!-- Takeaway start -->
<span id="takeaway">
<!-- The content in this section will be replaced with the content in takeaway-mobile.html if the browser's width is less than 1024px. -->
<section class="section hero is-small is-light">
<div class="container is-max-desktop">
<div class="content">
<h2 class="title is-3">How will data be used?</h2>
<p><b>Curated Dataset Benchmark (V0)</b></p>
<ul>
<li>~100-200 input/outputs categorized by cognitive process</li>
<li>LLM outputs interpreted by experts contributors who have advanced education in relevant field (professors, post docs, PhD candidates)</li>
<li>Strong theory, reliable</li>
</ul>
<p><b>Open Dataset Benchmark (V1)</b></p>
<ul>
<li>~500+ input/output</li>
<li>Data is provided by anyone*</li>
<li>Outputs can be useful to researchers in human-focused fields</li>
<li>Provide basis of cognitive LLM evaluation</li>
</ul>
<p><b>LLM Evaluation </b></p>
<ul>
<li>Compare LLM and human performance</li>
<li>Does model type, size affect different aspects of cognition?</li>
<li>Do different architectures have different cognitive capabilities?</li>
<li> Interpretability/explainability analysis</li>
</ul>
</div>
</div>
</div>
</div>
</ul>
</div>
</div>
</section>
</span>
<!-- Takeaway end -->
<!-- Limitations start
<section class="section hero is-small is-light" style="text-align: left;">
<div class="container is-max-desktop">
<div class="content">
<div class=" custom-text-left">
<h2 class="title is-3">Instructions for Contributors</h2>
<embed src="static/pdfs/instructions.pdf" width="700" height="375" type="application/pdf">
<p><a href="static/pdfs/instructions.pdf" target="_blank" class="external-link is-normal"><span> <b >Link to Instructions</b></span></a> </p>
</div>
</div>
</section>
-->
<!-- Limitations end -->
<!-- Limitations start -->
<section class="section hero is-small " style="text-align: left;">
<div class="container is-max-desktop">
<div class="content">
<div class=" custom-text-left">
<h2 class="title is-3">Primary Contacts</h2>
<p> Reach out if you have any questions, comments, or concerns!</p>
<p> Karin de Langis - <a href=mailto:[email protected]>[email protected]</a> </p>
<p> Dr. Dongyeop Kang - <a href=mailto:[email protected]>[email protected]</a></p>
</div>
</div>
</section>
<!-- Limitations end -->
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<div class="content">
<p>
Copyright @2024 <a href="https://minnesotanlp.github.io/">Minnesota NLP</a> group</a>
</p>
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