S oc RE val: Large Language Models with the Socratic Method for Reference-free Reasoning Evaluation (2024)

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 (**Soc**ratic Method-Inspired **R**easoning **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.

## introduction recent advances in large language models (llms) have led to state-of-the-art results in a plethora of natural language processing (nlp) tasks, demonstrating the effectiveness of in-context learning without the need for task-specific training or finetuning (openai, 2023; anil et al., 2023; tou-1 our code is publicly available at https://github.com/ hornhehhf/socreval . @xcite . despite these impressive achievements, the inherent reasoning capabilities of llms remain notably below human expectations @xcite . although the core of reasoning fundamentally involves offering justifications, most contemporary evaluations primarily assess a model's reasoning capability based on its end-task performance @xcite . such evaluations, focusing solely on the accuracy of the final answer, neglect the complexities of the underlying reasoning chains. this oversight inhibits a full understanding of a model's reasoning and challenges further progress in the field. to assess the quality of reasoning chains produced by models, a direct and intuitive approach centers on contrasting these generated chains with human-constructed ones, termed as referencebased reasoning evaluation @xcite @xcite . however, these reference-based metrics rely on humanconstructed reasoning chains as references, which are both labor-intensive and costly. furthermore, "gold-standard" reasoning chains may not be unique @xcite , implying that the effectiveness of reference-based evaluations can be significantly influenced by the choice and breadth of humancrafted references. in light of these challenges, recent research has begun to explore the evaluation of reasoning chains without necessitating humanannotated references-termed reference-free reasoning evaluation @xcite . regrettably, these reference-free metrics necessitate the fine-tuning of models on datasets with human-annotated reasoning chains before the evaluation, which are complicated and restricts their applicability across diverse datasets. in this work, we propose to harness the capabilities of llms to evaluate the model-generated reasoning chains, distinctively eliminating the necessity for both model fine-tuning and reliance on human-crafted reasoning chains. the appli- ## experiments in this section, we evaluate the performance of socreval on reference-free reasoning evaluation. in our experiments, we focus on gpt-4 (i.e., gpt-4-0613), eschewing assessments on smaller language models like gpt-3.5 and llama 2 @xcite . this decision is based on the anticipation of the emergence of more powerful llms in the near future, rendering the exploration of smaller language models less critical. ## analysis in this section, we undertake an in-depth analysis of socreval. for brevity and clarity, our primary emphasis centers on our best evaluation metric, socreval (all), often referred to simply as socreval. robustness analysis. prompting techniques, in certain contexts, have exhibited sensitivity to their specific phrasings @xcite . to discern the influence of prompt writing on socreval, we systematically rephrase prompt (5) five times. more details are in appendix a.1. by default, we utilize the first example from each dataset as the demonstration in the prompt. to systematically evaluate the influence of this demonstration exam-ple, we select five distinct examples at random for each dataset to serve as demonstrations. as shown in ## discussion in this work, we introduced a novel framework that harnesses the socratic method to craft optimized prompts for llms, specifically gpt-4, enabling enhanced assessment of reasoning chain quality. distinctively, our proposal avoids the need for model fine-tuning on human-crafted reasoning chains, a prevalent requirement in current reference-free reasoning evaluation metrics. the efficacy of our approach is substantiated across four datasets, wherein our proposed reference-free evaluation metric socreval demonstrates superior correlation with human judgments regarding reasoning chains produced by llms. comprehensive analyses further underscore the robustness of our method with respect to prompt writing and example selection, all while maintaining cost-efficiency. our study substantiates the efficacy of the socratic method in prompt design for reference-free reasoning evaluation, offering quantitative experimental results that demonstrate its applicability in reasoning tasks, in contrast to prior research that predominantly relied on qualitative case analyses. building on these findings, we anticipate several promising directions for future exploration. first, we simply select three strategies deeply relevant to our datasets; however, the expansive potential of the socratic method's strategies deserves a more comprehensive exploration. second, since evaluation plays a pivotal role in feedback mechanisms, a fusion of our proposal with prominent feedback integration methods like self-refine @xcite and @xcite could be useful in improving the reasoning capabilities of llms. moreover, considering the important role of reasoning chains in advanced prompting techniques-such as chain of thought, tree of thoughts @xcite , and graph of thoughts @xcite , we plan to harness our framework, llms with the socratic method, for an enriched understanding of these techniques, subsequently designing better prompts.