Making RALM Robust to Irrelevant Contexts via Layer Knowledge Guided Attention (2025.findings-acl)
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Weijie Shi, Hao Chen, Jiaming Li, Yao Zhao, Yazhong Zhang, Qijin Chen, Jipeng Zhang, Ruiyuan Zhang, Jia Zhu, Jiajie Xu, Xiaofang Zhou
| Challenge: | Large language models (LLMs) face factual hallucination and knowledge obsolescence when tackling knowledge-intensive tasks. |
| Approach: | They propose a layer-knowledge guided attention method which harnesses the layer-wise knowledge of large language models to optimize per-layer attention on useful passages. |
| Outcome: | The proposed method outperforms existing methods on RALM benchmarks. |
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