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index.html
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<!DOCTYPE html>
<html>
<head>
<script async src="https://www.googletagmanager.com/gtag/js?id=G-C1CRWDNJ1J"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'G-C1CRWDNJ1J');
</script>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0"><title>HF. 1 paper. January 20.</title>
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const articlesData = [{'id': 'https://huggingface.co/papers/2501.10132', 'title': 'ComplexFuncBench: Exploring Multi-Step and Constrained Function Calling under Long-Context Scenario', 'url': 'https://huggingface.co/papers/2501.10132', 'abstract': 'Enhancing large language models (LLMs) with real-time APIs can help generate more accurate and up-to-date responses. However, evaluating the function calling abilities of LLMs in real-world scenarios remains under-explored due to the complexity of data collection and evaluation. In this work, we introduce ComplexFuncBench, a benchmark for complex function calling across five real-world scenarios. Compared to existing benchmarks, ComplexFuncBench encompasses multi-step and constrained function calling, which requires long-parameter filing, parameter value reasoning, and 128k long context. Additionally, we propose an automatic framework, ComplexEval, for quantitatively evaluating complex function calling tasks. Through comprehensive experiments, we demonstrate the deficiencies of state-of-the-art LLMs in function calling and suggest future directions for optimizing these capabilities. The data and code are available at https://github.com/THUDM/ComplexFuncBench.', 'score': 0, 'issue_id': 1749, 'pub_date': '2025-01-17', 'pub_date_card': {'ru': '17 января', 'en': 'January 17', 'zh': '1月17日'}, 'hash': 'de405dcc4bfc8efc', 'authors': ['Lucen Zhong', 'Zhengxiao Du', 'Xiaohan Zhang', 'Haiyi Hu', 'Jie Tang'], 'affiliations': ['Tsinghua University', 'Zhipu AI'], 'pdf_title_img': 'assets/pdf/title_img/2501.10132.jpg', 'data': {'categories': ['#long_context', '#optimization', '#data', '#benchmark'], 'emoji': '🧪', 'ru': {'title': 'Новый бенчмарк для оценки сложных вызовов функций в больших языковых моделях', 'desc': 'Данная статья представляет новый бенчмарк ComplexFuncBench для оценки способностей больших языковых моделей (LLM) вызывать сложные функции в реальных сценариях. Бенчмарк включает в себя многошаговые и ограниченные вызовы функций, требующие заполнения длинных параметров и рассуждений о значениях параметров. Авторы также предлагают автоматическую систему ComplexEval для количественной оценки задач сложного вызова функций. Эксперименты показывают недостатки современных LLM в вызове функций и предлагают направления для оптимизации этих возможностей.'}, 'en': {'title': 'Benchmarking Complex Function Calling in LLMs', 'desc': 'This paper presents ComplexFuncBench, a new benchmark designed to evaluate the function calling abilities of large language models (LLMs) in real-world scenarios. It focuses on complex tasks that involve multi-step and constrained function calling, which require advanced reasoning and handling of long contexts. The authors also introduce ComplexEval, an automatic framework for quantitatively assessing these complex function calling tasks. Through their experiments, they highlight the limitations of current state-of-the-art LLMs and propose directions for improving their performance in this area.'}, 'zh': {'title': '提升LLMs函数调用能力的基准与评估', 'desc': '本论文提出了ComplexFuncBench，这是一个用于评估大型语言模型（LLMs）在复杂函数调用方面的基准测试。该基准涵盖了五种真实场景，涉及多步骤和受限的函数调用，要求模型进行长参数填写和参数值推理。我们还提出了ComplexEval，一个自动化框架，用于定量评估复杂函数调用任务的能力。通过实验，我们展示了当前最先进的LLMs在函数调用方面的不足，并提出了未来优化的方向。'}}}];
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document.getElementById('filter-logic-or').addEventListener('change', () => {
filterLogicIsAnd = false;
localStorage.setItem('filter_logic_is_and', 'false');
filterAndRenderArticles();
updateSelectedArticlesTitle();
});
function getUniqueCategories(articles) {
const categories = new Set();
articles.forEach(article => {
if (article.data && article.data.categories) {
article.data.categories.forEach(cat => categories.add(cat));
}
});
let res = Array.from(categories);
res.sort();
return res;
}
function createCategoryButtons() {
//const categories = getUniqueCategories(articlesData);
const categories = ['#3d', '#agents', '#agi', '#alignment', '#architecture', '#audio', '#benchmark (1)', '#cv', '#data (1)', '#dataset', '#diffusion', '#ethics', '#games', '#graphs', '#hallucinations', '#healthcare', '#inference', '#interpretability', '#leakage', '#long_context (1)', '#low_resource', '#machine_translation', '#math', '#multilingual', '#multimodal', '#open_source', '#optimization (1)', '#plp', '#rag', '#reasoning', '#rl', '#rlhf', '#robotics', '#science', '#security', '#small_models', '#story_generation', '#survey', '#synthetic', '#training', '#transfer_learning', '#video'];
categories.forEach(category => {
let catNameSplitted = category.split(/(\s+)/);
let catName = catNameSplitted[0];
const button = document.createElement('span');
button.textContent = catName;
button.className = 'category-button';
if (catNameSplitted.length < 2) {
button.classList.add('inactive');
};
button.onclick = () => toggleCategory(catName, button);
categoryFiltersContainer.appendChild(button);
});
}
function toggleCategory(category, button) {
const index = selectedCategories.indexOf(category);
if (index === -1) {
selectedCategories.push(category);
button.classList.add('active');
} else {
selectedCategories.splice(index, 1);
button.classList.remove('active');
}
filterAndRenderArticles();
saveCategorySelection();