@inproceedings{dou-etal-2025-lost,
title = "Lost in the Context: Insufficient and Distracted Attention to Contexts in Preference Modeling",
author = "Dou, Shihan and
Chen, Jiayi and
Huang, Chenhao and
Chen, Feng and
Chengzhi, Wei and
Zheng, Huiyuan and
Liu, Shichun and
Liu, Yan and
Liu, Chenxiao and
Xin, Chao and
Yan, Lin and
Zhang, Zongzhang and
Gui, Tao and
Zhang, Qi and
Huang, Xuanjing",
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.285/",
doi = "10.18653/v1/2025.acl-long.285",
pages = "5710--5728",
ISBN = "979-8-89176-251-0",
abstract = "In Reinforcement Learning from Human Feedback (RLHF), the reward model (RM) evaluates the response quality based on the given context and assigns a reward. It plays a crucial role in aligning RLHF with human preferences. Although the current RM training paradigm concatenates the context and response while amplifying the reward difference between good and bad response pairs, we demonstrate that the RM faces two significant issues: i) it often allocates only a small proportion of attention to the context, and ii) it frequently ignores segments of the context that are relevant for evaluating the response quality. These issues undermine the RM{'}s effectiveness in modeling human preferences. To further address these challenges, we propose AttnRM, a novel optimization framework that enables the RM to concentrate on crucial segments of the context. Experimental results demonstrate that AttnRM significantly improves preference modeling by increasing attention to relevant information within the context. It also enhances the RM{'}s generalizability and achieves better performance in aligning with human preferences."
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<abstract>In Reinforcement Learning from Human Feedback (RLHF), the reward model (RM) evaluates the response quality based on the given context and assigns a reward. It plays a crucial role in aligning RLHF with human preferences. Although the current RM training paradigm concatenates the context and response while amplifying the reward difference between good and bad response pairs, we demonstrate that the RM faces two significant issues: i) it often allocates only a small proportion of attention to the context, and ii) it frequently ignores segments of the context that are relevant for evaluating the response quality. These issues undermine the RM’s effectiveness in modeling human preferences. To further address these challenges, we propose AttnRM, a novel optimization framework that enables the RM to concentrate on crucial segments of the context. Experimental results demonstrate that AttnRM significantly improves preference modeling by increasing attention to relevant information within the context. It also enhances the RM’s generalizability and achieves better performance in aligning with human preferences.</abstract>
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%0 Conference Proceedings
%T Lost in the Context: Insufficient and Distracted Attention to Contexts in Preference Modeling
%A Dou, Shihan
%A Chen, Jiayi
%A Huang, Chenhao
%A Chen, Feng
%A Chengzhi, Wei
%A Zheng, Huiyuan
%A Liu, Shichun
%A Liu, Yan
%A Liu, Chenxiao
%A Xin, Chao
%A Yan, Lin
%A Zhang, Zongzhang
%A Gui, Tao
%A Zhang, Qi
%A Huang, Xuanjing
%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 dou-etal-2025-lost
%X In Reinforcement Learning from Human Feedback (RLHF), the reward model (RM) evaluates the response quality based on the given context and assigns a reward. It plays a crucial role in aligning RLHF with human preferences. Although the current RM training paradigm concatenates the context and response while amplifying the reward difference between good and bad response pairs, we demonstrate that the RM faces two significant issues: i) it often allocates only a small proportion of attention to the context, and ii) it frequently ignores segments of the context that are relevant for evaluating the response quality. These issues undermine the RM’s effectiveness in modeling human preferences. To further address these challenges, we propose AttnRM, a novel optimization framework that enables the RM to concentrate on crucial segments of the context. Experimental results demonstrate that AttnRM significantly improves preference modeling by increasing attention to relevant information within the context. It also enhances the RM’s generalizability and achieves better performance in aligning with human preferences.
%R 10.18653/v1/2025.acl-long.285
%U https://aclanthology.org/2025.acl-long.285/
%U https://doi.org/10.18653/v1/2025.acl-long.285
%P 5710-5728
Markdown (Informal)
[Lost in the Context: Insufficient and Distracted Attention to Contexts in Preference Modeling](https://aclanthology.org/2025.acl-long.285/) (Dou et al., ACL 2025)
ACL
- Shihan Dou, Jiayi Chen, Chenhao Huang, Feng Chen, Wei Chengzhi, Huiyuan Zheng, Shichun Liu, Yan Liu, Chenxiao Liu, Chao Xin, Lin Yan, Zongzhang Zhang, Tao Gui, Qi Zhang, and Xuanjing Huang. 2025. Lost in the Context: Insufficient and Distracted Attention to Contexts in Preference Modeling. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5710–5728, Vienna, Austria. Association for Computational Linguistics.