@inproceedings{wu-etal-2025-enhancing,
title = "Enhancing Mathematical Reasoning in {LLM}s by Stepwise Correction",
author = "Wu, Zhenyu and
Zeng, Qingkai and
Zhang, Zhihan and
Tan, Zhaoxuan and
Shen, Chao and
Jiang, Meng",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1048/",
doi = "10.18653/v1/2025.acl-long.1048",
pages = "21602--21623",
ISBN = "979-8-89176-251-0",
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 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{\%}. Our implementation is made publicly available at https://wzy6642.github.io/stepco.github.io."
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<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 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%. Our implementation is made publicly available at https://wzy6642.github.io/stepco.github.io.</abstract>
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%0 Conference Proceedings
%T Enhancing Mathematical Reasoning in LLMs by Stepwise Correction
%A Wu, Zhenyu
%A Zeng, Qingkai
%A Zhang, Zhihan
%A Tan, Zhaoxuan
%A Shen, Chao
%A Jiang, Meng
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F wu-etal-2025-enhancing
%X 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%. Our implementation is made publicly available at https://wzy6642.github.io/stepco.github.io.
%R 10.18653/v1/2025.acl-long.1048
%U https://aclanthology.org/2025.acl-long.1048/
%U https://doi.org/10.18653/v1/2025.acl-long.1048
%P 21602-21623
Markdown (Informal)
[Enhancing Mathematical Reasoning in LLMs by Stepwise Correction](https://aclanthology.org/2025.acl-long.1048/) (Wu et al., ACL 2025)
ACL
- Zhenyu Wu, Qingkai Zeng, Zhihan Zhang, Zhaoxuan Tan, Chao Shen, and Meng Jiang. 2025. Enhancing Mathematical Reasoning in LLMs by Stepwise Correction. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 21602–21623, Vienna, Austria. Association for Computational Linguistics.