SSR: Socratic Self-Refine for Large Language Model Reasoning

arXiv — cs.LG•Friday, November 14, 2025 at 5:00:00 AM

Large Language Models (LLMs) have shown impressive reasoning capabilities, but current frameworks for self-verification and correction are often inadequate for complex tasks. This paper introduces Socratic Self-Refine (SSR), a new framework designed for detailed evaluation and refinement of LLM reasoning. SSR breaks down model outputs into verifiable pairs of sub-questions and sub-answers, allowing for step-by-step confidence assessment through controlled re-solving and self-consistency checks. By identifying unreliable steps and refining them iteratively, SSR enhances the accuracy and interpretability of reasoning processes. Empirical tests across five reasoning benchmarks and three LLMs indicate that SSR consistently surpasses existing self-refinement methods. Additionally, SSR offers a systematic black-box approach for evaluating and comprehending LLM reasoning.

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