Step Guided Reasoning: Improving Mathematical Reasoning using Guidance Generation and Step Reasoning

ACL ARR 2025 February Submission318 Authors

Abstract: Mathematical reasoning has been challenging for large language models (LLMs). However, the introduction of step-by-step Chain-of-Thought (CoT) inference has significantly advanced the mathematical capabilities of LLMs.Despite this progress, current approaches either necessitate extensive inference datasets for training or depend on few-shot methods that frequently compromise computational accuracy. To address these bottlenecks in mathematical reasoning, we propose a novel method called \ourmethod, which is more stable and generalizable than few-shot methods and does not involve further fine-tuning of the model.In this approach, LLMs reflect on small reasoning steps, similar to how humans deliberate and focus attention on what to do next. By incorporating this reflective process into the inference stage, LLMs can effectively guide their reasoning from one step to the next.Through extensive experiments, we demonstrate the significant effect of \ourmethod in augmenting mathematical performance in state-of-the-art language models. Qwen2-72B-Instruct outperforms its math-specific counterpart, Qwen2.5-72B-Math-Instruct, on MMLU-STEM with a score of 90.9\%, compared to 87.3\%. The average scores of Qwen2-7B-Instruct and Qwen2-72B-Instruct increase from 27.1\% to 36.3\% and from 36.5\% to 47.4\% on the mathematics domain, respectively.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: Language Modeling

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 318