SocREval: Large Language Models with the Socratic Method for Reference-Free Reasoning Evaluation

• URL: http://arxiv.org/abs/2310.00074v2
• Date: Thu, 18 Apr 2024 21:53:10 GMT
• Title: SocREval: Large Language Models with the Socratic Method for Reference-Free Reasoning Evaluation
• Authors: Hangfeng He, Hongming Zhang, Dan Roth,
• Abstract summary: We develop SocREval, a novel approach for prompt design in reference-free reasoning evaluation. SocREval significantly improves GPT-4's performance, surpassing existing reference-free and reference-based reasoning evaluation metrics.
• Score: 78.23119125463964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To comprehensively gauge the capacity of current models for complex reasoning, it is crucial to assess their step-by-step reasoning in a scalable manner. Established reference-based evaluation metrics rely on human-annotated reasoning chains as references to assess the model-derived chains. However, such "gold-standard" human-written reasoning chains may not be unique and their acquisition is often labor-intensive. Existing reference-free reasoning evaluation metrics, while eliminating the need for human-crafted reasoning chains as references, often require fine-tuning with human-derived chains before evaluation, complicating the process and questioning their adaptability to other datasets. To address these challenges, we harness GPT-4 to automatically evaluate reasoning chain quality, thereby removing the dependency on human-written reasoning chains for both model fine-tuning and evaluative purposes. Leveraging the Socratic method, we develop SocREval ({\bf Soc}ratic Method-Inspired {\bf R}easoning {\bf Eval}uation), a novel approach for prompt design in reference-free reasoning evaluation. Empirical results from four human annotated datasets reveal that SocREval significantly improves GPT-4's performance, surpassing existing reference-free and reference-based reasoning evaluation metrics. Beyond its demonstrated efficacy, SocREval, proves to be both cost-efficient and robust to prompt writing and example selection, as substantiated by our in-depth analysis.

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