Year: 2025
Type: conference-paper
Abstract: 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 verifythen-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%.Our implementation is made publicly available at https: //wzy6642.github.io/stepco.github.io.
Authors: Zhenyu Wu, Qingkai Zeng, Zhihan Zhang, Zhaoxuan Tan, Chao Shen, Meng Jiang
Institutions: University of Notre Dame, Xi'an Jiaotong University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412944855