  The ability of Large Language Models (LLMs) to critique and refine their
reasoning is crucial for their application in evaluation, feedback provision,
and self-improvement. This paper introduces CriticBench, a comprehensive
benchmark designed to assess LLMs' abilities to critique and rectify their
reasoning across a variety of tasks. CriticBench encompasses five reasoning
domains: mathematical, commonsense, symbolic, coding, and algorithmic. It
compiles 15 datasets and incorporates responses from three LLM families.
Utilizing CriticBench, we evaluate and dissect the performance of 17 LLMs in
generation, critique, and correction reasoning, i.e., GQC reasoning. Our
findings reveal: (1) a linear relationship in GQC capabilities, with
critique-focused training markedly enhancing performance; (2) a task-dependent
variation in correction effectiveness, with logic-oriented tasks being more
amenable to correction; (3) GQC knowledge inconsistencies that decrease as
model size increases; and (4) an intriguing inter-model critiquing dynamic,
where stronger models are better at critiquing weaker ones, while weaker models
can surprisingly surpass stronger ones in their self-critique. We hope these
insights into the nuanced critique-correct reasoning of LLMs will foster
further research in LLM critique and self-improvement.
