解决大模型检索信息冲突问题，提升复杂场景下回答可靠性。
Accommodate Knowledge Conflicts in Retrieval-augmented LLMs: Towards Robust Response Generation in the Wild
- 从信息论角度分析冲突处理机制，发现差异显著时模型更自信
- 提出Swin-VIB框架，通过变分信息瓶颈适配检索差异，准确率领先
- 适合需要高可靠性的开放问答与多选任务，尤其在信息矛盾场景
大规模语言模型（LLMs）虽推动智能系统发展，但其内部记忆与外部检索信息常存在知识冲突，源于错误信息、偏见或过时内容。此类冲突降低回答可靠性并引入决策不确定性。本文从信息论视角分析模型如何应对冲突，发现当冲突与补充信息差异显著时，模型能自信决策并缓解不确定性；反之则产生明显困惑。基于此，提出Swin-VIB框架，集成变分信息瓶颈模型管道，动态调节检索信息差异，实现复杂冲突环境下的稳健生成。大量实验验证理论分析，并显示该方法在多选任务中精度超越所有基线，在开放式问答任务中EM值提升至少11.14%。
原文摘要 · Abstract (English)
The proliferation of large language models (LLMs) has significantly advanced intelligent systems. Unfortunately, LLMs often face knowledge conflicts between internal memory and retrieved external information, arising from misinformation, biases, or outdated knowledge. These conflicts undermine response reliability and introduce uncertainty in decision-making. In this work, we analyze how LLMs navigate knowledge conflicts from an information-theoretic perspective and reveal that when conflicting and supplementary information exhibit significant differences, LLMs confidently resolve their preferences and alleviate the uncertainty during their response generation. When this difference is ambiguous, LLMs experience considerable uncertainty about their generation. Based on this insight, we propose Swin-VIB, a novel framework that integrates a pipeline of variational information bottleneck models to adapt the retrieved information difference, facilitating robust response generation of LLMs even in conflicting contexts. Extensive experiments confirm our theoretical analysis and demonstrate the performance of Swin-VIB. Notably, Swin-VIB outperforms all competitive baselines in terms of the accuracy of the multiple-choice task, while improving the EM values in the open-ended QA task by at least 11.14%.
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