Critique ability are crucial in the scalable oversight and self-improvement of Large Language Models (LLMs). While many recent studies explore the critique ability of LLMs to judge and refine flaws in generations, how to comprehensively and reliably measure the critique abilities of LLMs is under-explored. This paper introduces \shortname, a novel benchmark designed to comprehensively and reliably evaluate four key critique ability dimensions of LLMs: feedback, comparison, refinement and meta-feedback. CriticBench encompasses nine diverse tasks, each assessing the LLMs' ability to critique responses at varying levels of quality granularity. Our extensive evaluations of open-source and closed-source LLMs reveal intriguing relationships between the critique ability and tasks, response qualities, and model scales. Datasets, resources and evaluation toolkit for CriticBench will be publicly released at \url{https://github.com/open-compass/CriticBench}.

翻译：批判能力对于大型语言模型的可扩展监督与自我改进至关重要。尽管近期诸多研究探索了LLMs评判与优化生成结果的批判能力，但如何全面可靠地衡量LLMs的批判能力仍缺乏深入探讨。本文提出新型基准测试\shortname，旨在全面可靠地评估LLMs的四大关键批判能力维度：反馈、比较、优化与元反馈。CriticBench包含九项多样化任务，每项任务均从不同质量粒度层面评估LLMs对生成结果的批判能力。我们对开源与闭源LLMs的广泛评估揭示了批判能力与任务类型、响应质量及模型规模之间的有趣关联。CriticBench的数据集、资源与评估工具包将发布于\url{https://github.com/open-compass/CriticBench}。