@inproceedings{wen-etal-2025-cheems,
title = "Cheems: A Practical Guidance for Building and Evaluating {C}hinese Reward Models from Scratch",
author = "Wen, Xueru and
Lou, Jie and
Li, Zichao and
Lu, Yaojie and
XingYu, XingYu and
Ji, Yuqiu and
Xu, Guohai and
Lin, Hongyu and
He, Ben and
Han, Xianpei and
Sun, Le and
Zhang, Debing",
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.737/",
doi = "10.18653/v1/2025.acl-long.737",
pages = "15187--15211",
ISBN = "979-8-89176-251-0",
abstract = "Reward models (RMs) are crucial for aligning large language models (LLMs) with human preferences. However, most RM research is centered on English and relies heavily on synthetic resources, which leads to limited and less reliable datasets and benchmarks for Chinese. To address this gap, we introduce CheemsBench, a fully human-annotated RM evaluation benchmark within Chinese contexts, and CheemsPreference, a large-scale and diverse preference dataset annotated through human-machine collaboration to support Chinese RM training. We systematically evaluate open-source discriminative and generative RMs on CheemsBench and observe significant limitations in their ability to capture human preferences in Chinese scenarios. Additionally, based on CheemsPreference, we construct an RM that achieves state-of-the-art performance on CheemsBench, demonstrating the necessity of human supervision in RM training. Our findings reveal that scaled AI-generated data struggles to fully capture human preferences, emphasizing the importance of high-quality human supervision in RM development."
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<abstract>Reward models (RMs) are crucial for aligning large language models (LLMs) with human preferences. However, most RM research is centered on English and relies heavily on synthetic resources, which leads to limited and less reliable datasets and benchmarks for Chinese. To address this gap, we introduce CheemsBench, a fully human-annotated RM evaluation benchmark within Chinese contexts, and CheemsPreference, a large-scale and diverse preference dataset annotated through human-machine collaboration to support Chinese RM training. We systematically evaluate open-source discriminative and generative RMs on CheemsBench and observe significant limitations in their ability to capture human preferences in Chinese scenarios. Additionally, based on CheemsPreference, we construct an RM that achieves state-of-the-art performance on CheemsBench, demonstrating the necessity of human supervision in RM training. Our findings reveal that scaled AI-generated data struggles to fully capture human preferences, emphasizing the importance of high-quality human supervision in RM development.</abstract>
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%0 Conference Proceedings
%T Cheems: A Practical Guidance for Building and Evaluating Chinese Reward Models from Scratch
%A Wen, Xueru
%A Lou, Jie
%A Li, Zichao
%A Lu, Yaojie
%A XingYu, XingYu
%A Ji, Yuqiu
%A Xu, Guohai
%A Lin, Hongyu
%A He, Ben
%A Han, Xianpei
%A Sun, Le
%A Zhang, Debing
%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 wen-etal-2025-cheems
%X Reward models (RMs) are crucial for aligning large language models (LLMs) with human preferences. However, most RM research is centered on English and relies heavily on synthetic resources, which leads to limited and less reliable datasets and benchmarks for Chinese. To address this gap, we introduce CheemsBench, a fully human-annotated RM evaluation benchmark within Chinese contexts, and CheemsPreference, a large-scale and diverse preference dataset annotated through human-machine collaboration to support Chinese RM training. We systematically evaluate open-source discriminative and generative RMs on CheemsBench and observe significant limitations in their ability to capture human preferences in Chinese scenarios. Additionally, based on CheemsPreference, we construct an RM that achieves state-of-the-art performance on CheemsBench, demonstrating the necessity of human supervision in RM training. Our findings reveal that scaled AI-generated data struggles to fully capture human preferences, emphasizing the importance of high-quality human supervision in RM development.
%R 10.18653/v1/2025.acl-long.737
%U https://aclanthology.org/2025.acl-long.737/
%U https://doi.org/10.18653/v1/2025.acl-long.737
%P 15187-15211
Markdown (Informal)
[Cheems: A Practical Guidance for Building and Evaluating Chinese Reward Models from Scratch](https://aclanthology.org/2025.acl-long.737/) (Wen et al., ACL 2025)
ACL
- Xueru Wen, Jie Lou, Zichao Li, Yaojie Lu, XingYu XingYu, Yuqiu Ji, Guohai Xu, Hongyu Lin, Ben He, Xianpei Han, Le Sun, and Debing Zhang. 2025. Cheems: A Practical Guidance for Building and Evaluating Chinese Reward Models from Scratch. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15187–15211, Vienna, Austria. Association for Computational Linguistics.