C ritic B ench: Benchmarking LLM s for Critique-Correct Reasoning (2024)

Abstract: 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.

## introduction the advent of large language models (llms) has revolutionized artificial intelligence, showcasing remarkable proficiency across diverse tasks @xcite @xcite @xcite ll a m a -2 -7 b v ic u n a -7 b ll a m a -2 -1 3 b v ic u n a -1 3 b v ic u n a -3 3 b ll a m a -2 -7 0 b g p t-3 . figure 1 : knowledge consistency across a model's three abilities: generation (g), critique (q), and correction (c). it shows the overlap of correct responses by the model for a given question across these tasks. however, a comprehensive understanding of llms' critical reasoning abilities remains elusive. prior research @xcite @xcite has focused on a narrow range of models and datasets and has yielded inconsistent findings @xcite , underscoring the need for a thorough investigation. it is imperative to systematically gauge llms' proficiency in critiquing and correcting provided answers. to address these challenges, we present crit-icbench, a comprehensive benchmark designed to evaluate the critique and correction skills of llms. criticbench encompasses 15 datasets spanning five task categories: mathematical, commonsense, symbolic, coding, and algorithmic. we leverage eight models from the llama, vicuna, and gpt series to create the responses that are to be critiqued and corrected. moreover, we include gpt-4 and undertake manual data reviews to ensure data quality, culminating in 3.8k data instances (see our contributions are summarized as follows: ## experiments 4.1 experimental setup to conduct a comprehensive assessment, we have selected the following models: phi-2, the llama family, the vicuna family, the mistral family, and the gpt family. we evaluate those models on criticbench under three phases: (1) generation: llms are tasked with answering questions through the utilization of cot @xcite , during which accuracy serves as the performance indicator. the prompts for above phases are detailed in appendix g. for all experimental setups, we set the temperature to 0 during the three phases. ## conclusion in conclusion, our investigation through crit-icbench has illuminated the capacities and limitations of llms in gqc reasoning. our focused exploration using criticbench on the relationship among models in gqc revealed a linear correlation and subtle inconsistencies between gqc, while our analysis across different task types found that models perform better in q and c for tasks focused on logic compared to those requiring attention to detail. additionally, by examining the outcomes of models critiquing each other, we discovered that weaker models could sometimes correct the outputs of stronger models more effectively than those models could self-correct. these findings underscore the effectiveness of criticbench in evaluating and analyzing the gqc capabilities of llms.