@inproceedings{yuan-etal-2025-exploiting,
title = "Exploiting Contextual Knowledge in {LLM}s through $\mathcal{V}$-usable Information based Layer Enhancement",
author = "Yuan, Xiaowei and
Yang, Zhao and
Huang, Ziyang and
Wang, Yequan and
Fan, Siqi and
Ju, Yiming and
Zhao, Jun and
Liu, Kang",
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.1531/",
doi = "10.18653/v1/2025.acl-long.1531",
pages = "31726--31741",
ISBN = "979-8-89176-251-0",
abstract = "Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, yet they often struggle with context-faithfulness generations that properly reflect contextual knowledge. While existing approaches focus on enhancing the decoding strategies, they ignore the fundamental mechanism of how contextual information is processed within LLMs' internal states. As a result, LLMs remain limited in their ability to fully leverage contextual knowledge. In this paper, we propose Context-aware Layer Enhancement (CaLE), a novel intervention method that enhances the utilization of contextual knowledge within LLMs' internal representations. By employing $\mathcal{V}$-usable information analysis, CaLE strategically amplifies the growth of contextual information at an optimal layer, thereby enriching representations in the final layer. Our experiments demonstrate that CaLE effectively improves context-faithful generation in Question-Answering tasks, particularly in scenarios involving unknown or conflicting contextual knowledge."
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<abstract>Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, yet they often struggle with context-faithfulness generations that properly reflect contextual knowledge. While existing approaches focus on enhancing the decoding strategies, they ignore the fundamental mechanism of how contextual information is processed within LLMsā internal states. As a result, LLMs remain limited in their ability to fully leverage contextual knowledge. In this paper, we propose Context-aware Layer Enhancement (CaLE), a novel intervention method that enhances the utilization of contextual knowledge within LLMsā internal representations. By employing \mathcalV-usable information analysis, CaLE strategically amplifies the growth of contextual information at an optimal layer, thereby enriching representations in the final layer. Our experiments demonstrate that CaLE effectively improves context-faithful generation in Question-Answering tasks, particularly in scenarios involving unknown or conflicting contextual knowledge.</abstract>
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%0 Conference Proceedings
%T Exploiting Contextual Knowledge in LLMs through \mathcalV-usable Information based Layer Enhancement
%A Yuan, Xiaowei
%A Yang, Zhao
%A Huang, Ziyang
%A Wang, Yequan
%A Fan, Siqi
%A Ju, Yiming
%A Zhao, Jun
%A Liu, Kang
%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 yuan-etal-2025-exploiting
%X Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, yet they often struggle with context-faithfulness generations that properly reflect contextual knowledge. While existing approaches focus on enhancing the decoding strategies, they ignore the fundamental mechanism of how contextual information is processed within LLMsā internal states. As a result, LLMs remain limited in their ability to fully leverage contextual knowledge. In this paper, we propose Context-aware Layer Enhancement (CaLE), a novel intervention method that enhances the utilization of contextual knowledge within LLMsā internal representations. By employing \mathcalV-usable information analysis, CaLE strategically amplifies the growth of contextual information at an optimal layer, thereby enriching representations in the final layer. Our experiments demonstrate that CaLE effectively improves context-faithful generation in Question-Answering tasks, particularly in scenarios involving unknown or conflicting contextual knowledge.
%R 10.18653/v1/2025.acl-long.1531
%U https://aclanthology.org/2025.acl-long.1531/
%U https://doi.org/10.18653/v1/2025.acl-long.1531
%P 31726-31741
Markdown (Informal)
[Exploiting Contextual Knowledge in LLMs through š±-usable Information based Layer Enhancement](https://aclanthology.org/2025.acl-long.1531/) (Yuan et al., ACL 2025)
ACL
- Xiaowei Yuan, Zhao Yang, Ziyang Huang, Yequan Wang, Siqi Fan, Yiming Ju, Jun Zhao, and Kang Liu. 2025. Exploiting Contextual Knowledge in LLMs through š±-usable Information based Layer Enhancement. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 31726ā31741, Vienna, Austria. Association for Computational Linguistics.