RealCritic: Towards Effectiveness-Driven Evaluation of Language Model
  Critiques
Authors: name: Zhengyang Tang, name: Ziniu Li, name: Zhenyang Xiao, name: Tian Ding, name: Ruoyu Sun, name: Benyou Wang, name: Dayiheng Liu, name: Fei Huang, name: Tianyu Liu, name: Bowen Yu, name: Junyang Lin

Critiques are important for enhancing the performance of Large Language
Models (LLMs), enabling both self-improvement and constructive feedback for
others by identifying flaws and suggesting improvements. However, evaluating
the critique capabilities of LLMs presents a significant challenge due to the
open-ended nature of the task. In this work, we introduce a new benchmark
designed to assess the critique capabilities of LLMs. Unlike existing
benchmarks, which typically function in an open-loop fashion, our approach
employs a closed-loop methodology that evaluates the quality of corrections
generated from critiques. Moreover, the benchmark incorporates features such as
self-critique, cross-critique, and iterative critique, which are crucial for
distinguishing the abilities of advanced reasoning models from more classical
ones. We implement this benchmark using eight challenging reasoning tasks. We
have several interesting findings. First, despite demonstrating comparable
performance in direct chain-of-thought generation, classical LLMs significantly
lag behind the advanced reasoning-based model o1-mini across all critique
scenarios. Second, in self-critique and iterative critique settings, classical
LLMs may even underperform relative to their baseline capabilities. We hope
that this benchmark will serve as a valuable resource to guide future
advancements. The code and data are available at
https://github.com/tangzhy/RealCritic.