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 CriticBench, 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 https://github.com/open-compass/CriticBench.

翻译：批判能力在大语言模型（LLMs）的可扩展监督与自我改进中至关重要。尽管近期研究广泛探索了LLMs对生成内容进行评判与缺陷修正的批判能力，但如何全面可靠地测量LLMs的批判能力仍待深入探究。本文提出CriticBench——一个旨在全面可靠评估LLMs四大关键批判能力维度（反馈、比较、精炼与元反馈）的新型基准测试。CriticBench包含九个多样化任务，每个任务均在不同质量粒度层级上评估LLMs对回答的批判能力。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、回答质量及模型规模之间的有趣关联。CriticBench的数据集、资源及评估工具包将在https://github.com/open-compass/CriticBench公开。