The papers are organized according to our survey:
<p align="center"><strong>Evaluating Large Language Models: A Comprehensive Survey</strong></p> <p align="center">Zishan Guo*, Renren Jin*, Chuang Liu*, Yufei Huang, Dan Shi, Supryadi, </p> <p align="center">Linhao Yu, Yan Liu, Jiaxuan Li, Bojian Xiong, Deyi Xiong†</p> <p align="center">Tianjin University</p> <p align="center">(*: Co-first authors, †: Corresponding author)</p> <div align=center> <img src="./imgs/Figure_1.png" style="zoom:30%"/> </div>If you find our survey useful, please kindly cite our paper:
@article{guo2023evaluating, title={Evaluating Large Language Models: A Comprehensive Survey}, author={Guo, Zishan and Jin, Renren and Liu, Chuang and Huang, Yufei and Shi, Dan and Yu, Linhao and Liu, Yan and Li, Jiaxuan and Xiong, Bojian and Xiong, Deyi and others}, journal={arXiv preprint arXiv:2310.19736}, year={2023} }
Feel free to open an issue/PR or e-mail guozishan@tju.edu.cn, rrjin@tju.edu.cn, liuc_09@tju.edu.cn and dyxiong@tju.edu.cn if you find any missing areas, papers, or datasets. We will keep updating this list and survey.
Large language models (LLMs) have demonstrated remarkable capabilities across a broad spectrum of tasks. They have attracted significant attention and been deployed in numerous downstream applications. Nevertheless, akin to a double-edged sword, LLMs also present potential risks. They could suffer from private data leaks or yield inappropriate, harmful, or misleading content. Additionally, the rapid progress of LLMs raises concerns about the potential emergence of superintelligent systems without adequate safeguards. To effectively capitalize on LLM capacities as well as ensure their safe and beneficial development, it is critical to conduct a rigorous and comprehensive evaluation of LLMs.
This survey endeavors to offer a panoramic perspective on the evaluation of LLMs. We categorize the evaluation of LLMs into three major groups: knowledge and capability evaluation, alignment evaluation and safety evaluation. In addition to the comprehensive review on the evaluation methodologies and benchmarks on these three aspects, we collate a compendium of evaluations pertaining to LLMs' performance in specialized domains, and discuss the construction of comprehensive evaluation platforms that covers LLM evaluations on capabilities, alignment, safety, sand applicability.
We hope that this comprehensive overview will stimulate further research interests in the evaluation of LLMs, with the ultimate goal of making evaluation serve as a cornerstone in guiding the responsible development of LLMs. We envision that this will channel their evolution into a direction that maximizes societal benefit while minimizing potential risks.
The paper proposes a dataset that can be used for LLMs evaluation.
The paper proposes an evaluation method that can be used for LLMs.
The paper proposes a platform for LLMs evaluation.
The paper examines the performance of LLMs in a particular domain.
"Through the Lens of Core Competency: Survey on Evaluation of Large Language Models".
"A Survey on Evaluation of Large Language Models".
Yupeng Chang and Xu Wang et al. arXiv 2023. [Paper] [GitHub]
Squad: "Squad: 100, 000+ questions for machine comprehension of text".
NarrativeQA: "The narrativeqa reading comprehension challenge".
Hotpotqa: "Hotpotqa: A dataset for diverse, explainable multi-hop question answering".
CoQA: "Coqa: A conversational question answering challenge".
NQ: "Natural questions: a benchmark for question answering research".
DuReader: "Dureader_robust: A chinese dataset towards evaluating robustness and generalization of machine reading comprehension in real-world applications".
RAGAS: "RAGAS: Automated Evaluation of Retrieval Augmented Generation".
"Why Does ChatGPT Fall Short in Providing Truthful Answers?".
Shen Zheng and Jie Huang et al. arXiv 2023. [Paper]
LAMA: "Language Models as Knowledge Bases?".
Kola: "Kola: Carefully Benchmarking World Knowledge of Large Language models".
WikiFact: "Assessing the Factual Accuracy of Generated Text".
Ben Goodrich et al. KDD 2019. [Paper]
ARC: "Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge".
QASC: "QASC: A Dataset for Question Answering via Sentence Composition".
MCTACO: ""Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding".
TRACIE: "Temporal Reasoning on Implicit Events from Distant Supervision".
TIMEDIAL: "TIMEDIAL: Temporal Commonsense Reasoning in Dialog".
HellaSWAG: "HellaSwag: Can a Machine Really Finish Your Sentence?".
PIQA: "PIQA: Reasoning about Physical Commonsense in Natural Language".
Pep-3k: "Modeling Semantic Plausibility by Injecting World Knowledge".
Social IQA: "Social IQa: Commonsense Reasoning about Social Interactions".
Maarten Sap and Hannah Rashkin et al. EMNLP 2019. [Paper] [Source]
CommonsenseQA: "CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge".
Alon Talmor and Jonathan Herzig et al. NAACL 2019. [Paper] [GitHub]
OpenBookQA: "Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering".
"A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity".
"ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models".
Ning Bian et al. arXiv 2023. [Paper]
SNLI: "A large annotated corpus for learning natural language inference".
Samuel R. Bowman et al. EMNLP 2015. [Paper]
MultiNLI: "A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference".
LogicNLI: "Diagnosing the First-Order Logical Reasoning Ability Through LogicNLI".
Jidong Tian and Yitian Li et al. EMNLP 2021.
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UI-TARS-desktop 是一款功能强大的桌面应用,基于 UI-TARS(视觉语言模型)构建。它具备自然语言控制、截图与视觉识别、精确的鼠标键盘控制等功能,支持跨平台使用(Windows/MacOS),能提供实时反馈和状态显示,且数据完全本地处理,保障隐私安全。该应用集成了多种大语言模型和搜索方式,还可进行文件系统操作。适用于需要智能交互和自动化任务的场景,如信息检索、文件管理等。其提供了详细的文档,包括快速启动、部署、贡献指南和 SDK 使用说明等,方便开发者使用和扩展。
开源且先进的大规模视频生成模型项目
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一款强大的视觉语言模型,支持图像和视频输入
Qwen2.5-VL 是一款强大的视觉语言模型,支持图像和视频输入,可用于多种场景,如商品特点总结、图像文字识别等。项目提供了 OpenAI API 服务、Web UI 示例等部署方式,还包含了视觉处理工具,有助于开发者快速集成和使用,提升工作效率。
HunyuanVideo 是一个可基于文本生成高质量图像和视频的项目。
HunyuanVideo 是一个专注于文本到图像及视频生成的项目。它具备强大的视频生成能力,支持多种分辨率和视频长度选择,能根据用户输入的文本生成逼真的图像和视频。使用先进的技术架构和算法,可灵活调整生成参数,满足不同场景的需求,是文本生成图像视 频领域的优质工具。
一个基于 Gradio 构建的 WebUI,支持与浏览器智能体进行便捷交互。
WebUI for Browser Use 是一个强大的项目,它集成了多种大型语言模型,支持自定义浏览器使用,具备持久化浏览器会话等功能。用户可以通过简洁友好的界面轻松控制浏览器智能体完成各类任务,无论是数据提取、网页导航还是表单填写等操作都能高效实现,有利于提高工作效率和获取信息的便捷性。该项目适合开发者、研究人员以及需要自动化浏览器操作的人群使用,在 SEO 优化方面,其关键词涵盖浏览器使用、WebUI、大型语言模型集成等,有助于提高网页在搜索引擎中的曝光度。
基于 ESP32 的小智 AI 开发项目,支持多种网络连接与协议,实现语音交互等功能。
xiaozhi-esp32 是一个极具创新性的基于 ESP32 的开发项目,专注于人工智能语音交互领域。项目涵盖了丰富的功能,如网络连接、OTA 升级、设备激活等,同时支持多种语言。无论是开发爱好者还是专业开发者,都能借助该项目快速搭建起高效的 AI 语音交互系统,为智能设备开发提供强大助力。
一个用于 OCR 的项目,支持多种模型和服务器进行 PDF 到 Markdown 的转换,并提供测试和报告功能。
olmocr 是一个专注于光学字符识别(OCR)的 Python 项目,由 Allen Institute for Artificial Intelligence 开发。它支持多种模型和服务器,如 vllm、sglang、OpenAI 等,可将 PDF 文件的页面转换为 Markdown 格式。项目还提供了测试框架和 HTML 报告生成功能,方便用户对 OCR 结果进行评估和分析。适用于科研、文档处理等领域,有助于提高工作效率和准确性。
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