研究大模型偏好的外部知识特征，提升多跳问答准确性。
What External Knowledge is Preferred by LLMs? Characterizing and Exploring Chain of Evidence in Imperfect Context for Multi-Hop QA
- 基于证据链（CoE）框架，识别模型偏好具有相关性与互证性的知识。
- 在不完美上下文中，采用CoE特征的知识使模型准确率显著提升。
- 适合关注知识检索、对抗中毒防御的研究者参考。
引入外部知识是缓解大模型过时信息与幻觉问题的可行路径，但外部知识常含冗余或错误内容，干扰模型对有效信息的利用。本文旨在刻画大模型偏好的外部知识特征，并在不完美上下文中开展实证研究。受证据链（CoE）启发，我们提出知识应同时具备问题相关性与文本间互证性。通过对比CoE与非CoE样本在显著性、欺骗性和鲁棒性方面的表现，揭示模型对符合CoE特征知识的偏好。进一步选取三种代表性任务（基于RAG的多跳问答、外部知识投毒与防御），结合现有最优或主流基线，引入CoE特征后，改进版本在各项任务中均取得显著性能提升。
原文摘要 · Abstract (English)
Incorporating external knowledge has emerged as a promising way to mitigate outdated knowledge and hallucinations in LLM. However, external knowledge is often imperfect, encompassing substantial extraneous or even inaccurate content, which interferes with the LLM's utilization of useful knowledge in the context. This paper seeks to characterize the features of preferred external knowledge and perform empirical studies in imperfect contexts. Inspired by the chain of evidence (CoE), we characterize that the knowledge preferred by LLMs should maintain both relevance to the question and mutual support among the textual pieces. Accordingly, we propose a CoE discrimination approach and conduct a comparative analysis between CoE and Non-CoE samples across significance, deceptiveness, and robustness, revealing the LLM's preference for external knowledge that aligns with CoE features. Furthermore, we selected three representative tasks (RAG-based multi-hop QA, external knowledge poisoning and poisoning defense), along with corresponding SOTA or prevalent baselines. By integrating CoE features, the variants achieved significant improvements over the original baselines.
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