Enhancing Mathematical Reasoning in LLMs by Stepwise Correction
This addresses the issue of inefficient and error-prone reasoning in LLMs for mathematical problem-solving, representing a strong incremental improvement over existing methods.
The paper tackles the problem of repeated errors in Best-of-N decoding for mathematical reasoning in LLMs by proposing Stepwise Correction (StepCo), a prompting method that iteratively verifies and revises reasoning steps, achieving an average accuracy of 94.1% across eight datasets, outperforming the state-of-the-art by +2.4% and reducing token consumption by 77.8%.
Best-of-N decoding methods instruct large language models (LLMs) to generate multiple solutions, score each using a scoring function, and select the highest scored as the final answer to mathematical reasoning problems. However, this repeated independent process often leads to the same mistakes, making the selected solution still incorrect. We propose a novel prompting method named Stepwise Correction (StepCo) that helps LLMs identify and revise incorrect steps in their generated reasoning paths. It iterates verification and revision phases that employ a process-supervised verifier. The verify-then-revise process not only improves answer correctness but also reduces token consumption with fewer paths needed to generate. With StepCo, a series of LLMs demonstrate exceptional performance. Notably, using GPT-4o as the backend LLM, StepCo achieves an average accuracy of 94.1 across eight datasets, significantly outperforming the state-of-the-art Best-of-N method by +2.4, while reducing token consumption by 77.8%.