@inproceedings{shi-etal-2025-making,
title = "Making {RALM} Robust to Irrelevant Contexts via Layer Knowledge Guided Attention",
author = "Shi, Weijie and
Chen, Hao and
Li, Jiaming and
Zhao, Yao and
Zhang, Yazhong and
Chen, Qijin and
Zhang, Jipeng and
Zhang, Ruiyuan and
Zhu, Jia and
Xu, Jiajie and
Zhou, Xiaofang",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.188/",
doi = "10.18653/v1/2025.findings-acl.188",
pages = "3652--3668",
ISBN = "979-8-89176-256-5",
abstract = "Retrieval-augmented language models (RALMs) aim to incorporate external knowledge to address the issues of factual hallucination and knowledge obsolescence faced by large language models (LLMs). Inevitably, the retrieved passages based on similarity search may be irrelevant to the given question, and the aggregation of these passages can confuse the model to give a correct answer. To improve the performance of RALM in such conditions, we propose layer-knowledge guided attention for RALMs, which harnesses the layer-wise knowledge of LLMs to optimize per-layer attention on useful passages, making the model pay attention to the most relevant content and ignore irrelevant ones. Specifically, we first systematically study LLM{'}s attention patterns and their relationship with the accuracy of RALM responses, where middle-focus attentions play a crucial role in selectively gathering relevant information. Based on this, a layer-wise passage estimator leverages the varied knowledge encoded across LLM layers to assess not only passage relevance scores but also associated confidences. Finally, a relevance-aware passage fusion enables selective attention to relevant passages, mitigating distractibility and positional bias of causal attention. Experiments show that our method outperforms existing methods on RALM benchmarks."
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<abstract>Retrieval-augmented language models (RALMs) aim to incorporate external knowledge to address the issues of factual hallucination and knowledge obsolescence faced by large language models (LLMs). Inevitably, the retrieved passages based on similarity search may be irrelevant to the given question, and the aggregation of these passages can confuse the model to give a correct answer. To improve the performance of RALM in such conditions, we propose layer-knowledge guided attention for RALMs, which harnesses the layer-wise knowledge of LLMs to optimize per-layer attention on useful passages, making the model pay attention to the most relevant content and ignore irrelevant ones. Specifically, we first systematically study LLM’s attention patterns and their relationship with the accuracy of RALM responses, where middle-focus attentions play a crucial role in selectively gathering relevant information. Based on this, a layer-wise passage estimator leverages the varied knowledge encoded across LLM layers to assess not only passage relevance scores but also associated confidences. Finally, a relevance-aware passage fusion enables selective attention to relevant passages, mitigating distractibility and positional bias of causal attention. Experiments show that our method outperforms existing methods on RALM benchmarks.</abstract>
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%0 Conference Proceedings
%T Making RALM Robust to Irrelevant Contexts via Layer Knowledge Guided Attention
%A Shi, Weijie
%A Chen, Hao
%A Li, Jiaming
%A Zhao, Yao
%A Zhang, Yazhong
%A Chen, Qijin
%A Zhang, Jipeng
%A Zhang, Ruiyuan
%A Zhu, Jia
%A Xu, Jiajie
%A Zhou, Xiaofang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F shi-etal-2025-making
%X Retrieval-augmented language models (RALMs) aim to incorporate external knowledge to address the issues of factual hallucination and knowledge obsolescence faced by large language models (LLMs). Inevitably, the retrieved passages based on similarity search may be irrelevant to the given question, and the aggregation of these passages can confuse the model to give a correct answer. To improve the performance of RALM in such conditions, we propose layer-knowledge guided attention for RALMs, which harnesses the layer-wise knowledge of LLMs to optimize per-layer attention on useful passages, making the model pay attention to the most relevant content and ignore irrelevant ones. Specifically, we first systematically study LLM’s attention patterns and their relationship with the accuracy of RALM responses, where middle-focus attentions play a crucial role in selectively gathering relevant information. Based on this, a layer-wise passage estimator leverages the varied knowledge encoded across LLM layers to assess not only passage relevance scores but also associated confidences. Finally, a relevance-aware passage fusion enables selective attention to relevant passages, mitigating distractibility and positional bias of causal attention. Experiments show that our method outperforms existing methods on RALM benchmarks.
%R 10.18653/v1/2025.findings-acl.188
%U https://aclanthology.org/2025.findings-acl.188/
%U https://doi.org/10.18653/v1/2025.findings-acl.188
%P 3652-3668
Markdown (Informal)
[Making RALM Robust to Irrelevant Contexts via Layer Knowledge Guided Attention](https://aclanthology.org/2025.findings-acl.188/) (Shi et al., Findings 2025)
ACL
- Weijie Shi, Hao Chen, Jiaming Li, Yao Zhao, Yazhong Zhang, Qijin Chen, Jipeng Zhang, Ruiyuan Zhang, Jia Zhu, Jiajie Xu, and Xiaofang Zhou. 2025. Making RALM Robust to Irrelevant Contexts via Layer Knowledge Guided Attention. In Findings of the Association for Computational Linguistics: ACL 2025, pages 3652–3668, Vienna, Austria. Association for Computational Linguistics.