@inproceedings{wang-etal-2024-math,
title = "Math-Shepherd: Verify and Reinforce {LLM}s Step-by-step without Human Annotations",
author = "Wang, Peiyi and
Li, Lei and
Shao, Zhihong and
Xu, Runxin and
Dai, Damai and
Li, Yifei and
Chen, Deli and
Wu, Yu and
Sui, Zhifang",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.510",
doi = "10.18653/v1/2024.acl-long.510",
pages = "9426--9439",
abstract = "In this paper, we present an innovative process-oriented math process reward model called Math-shepherd, which assigns a reward score to each step of math problem solutions. The training of Math-shepherd is achieved using automatically constructed process-wise supervision data, breaking the bottleneck of heavy reliance on manual annotation in existing work. We explore the effectiveness of Math-shepherd in two scenarios: 1) $\textit{Verification}$: Math-shepherd is utilized for reranking multiple outputs generated by Large Language Models (LLMs); 2) $\textit{Reinforcement Learning (RL)}$: Math-shepherd is employed to reinforce LLMs.With Math-shepherd, a series of open-source LLMs demonstrates exceptional performance. For instance, process RL with Math-shepherd significantly enhances Mistral-7B (77.9{\%}$\to$84.1{\%} on GSM8K and 28.6{\%}$\to$33.0{\%} on MATH).The accuracy can be further improved to 89.1{\%} and 43.5{\%} on two benchmarks with verification of Math-shepherd.We believe that automatic process supervision holds significant potential for the future evolution of LLMs.",
}
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<abstract>In this paper, we present an innovative process-oriented math process reward model called Math-shepherd, which assigns a reward score to each step of math problem solutions. The training of Math-shepherd is achieved using automatically constructed process-wise supervision data, breaking the bottleneck of heavy reliance on manual annotation in existing work. We explore the effectiveness of Math-shepherd in two scenarios: 1) Verification: Math-shepherd is utilized for reranking multiple outputs generated by Large Language Models (LLMs); 2) Reinforcement Learning (RL): Math-shepherd is employed to reinforce LLMs.With Math-shepherd, a series of open-source LLMs demonstrates exceptional performance. For instance, process RL with Math-shepherd significantly enhances Mistral-7B (77.9%84.1% on GSM8K and 28.6%33.0% on MATH).The accuracy can be further improved to 89.1% and 43.5% on two benchmarks with verification of Math-shepherd.We believe that automatic process supervision holds significant potential for the future evolution of LLMs.</abstract>
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%0 Conference Proceedings
%T Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
%A Wang, Peiyi
%A Li, Lei
%A Shao, Zhihong
%A Xu, Runxin
%A Dai, Damai
%A Li, Yifei
%A Chen, Deli
%A Wu, Yu
%A Sui, Zhifang
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F wang-etal-2024-math
%X In this paper, we present an innovative process-oriented math process reward model called Math-shepherd, which assigns a reward score to each step of math problem solutions. The training of Math-shepherd is achieved using automatically constructed process-wise supervision data, breaking the bottleneck of heavy reliance on manual annotation in existing work. We explore the effectiveness of Math-shepherd in two scenarios: 1) Verification: Math-shepherd is utilized for reranking multiple outputs generated by Large Language Models (LLMs); 2) Reinforcement Learning (RL): Math-shepherd is employed to reinforce LLMs.With Math-shepherd, a series of open-source LLMs demonstrates exceptional performance. For instance, process RL with Math-shepherd significantly enhances Mistral-7B (77.9%84.1% on GSM8K and 28.6%33.0% on MATH).The accuracy can be further improved to 89.1% and 43.5% on two benchmarks with verification of Math-shepherd.We believe that automatic process supervision holds significant potential for the future evolution of LLMs.
%R 10.18653/v1/2024.acl-long.510
%U https://aclanthology.org/2024.acl-long.510
%U https://doi.org/10.18653/v1/2024.acl-long.510
%P 9426-9439
Markdown (Informal)
[Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations](https://aclanthology.org/2024.acl-long.510) (Wang et al., ACL 2024)
ACL
- Peiyi Wang, Lei Li, Zhihong Shao, Runxin Xu, Damai Dai, Yifei Li, Deli Chen, Yu Wu, and Zhifang Sui. 2024. Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9426–9439, Bangkok, Thailand. Association for Computational Linguistics.