LLMSR@XLLM25: SWRV: Empowering Self-Verification of Small Language Models through Step-wise Reasoning and Verification

Large language models (LLMs) have demonstrated impressive reasoning capabilities through Chain-of-Thought (CoT) prompting.However, their reasoning processes remain inexplicable and uncontrollable.In this paper, we introduce a Step-Wise Reasoning and Verification (SWRV) framework, designed as a two-stage Parser-Verifier pipeline, that decomposes the reasoning process into discrete inference steps and rigorously validates each one.Our Parser extracts problem constraints and the sequence of reasoning steps from the LLM's output, and our Verifier, either LM-based or powered by a symbolic solver, checks the logical correctness of every step.To ensure robust parsing, we fine-tune a compact LM on a small, high-quality annotation set generated by a more capable LM.Experiments on the LLMSR dataset built atop LogiQA show significant gains over baselines, illustrating the effectiveness of our method for step-wise analysis of LLM reasoning 1 .