This survey provides an in-depth analysis of knowledge conflicts for large language models (LLMs), highlighting the complex challenges they encounter when blending contextual and parametric knowledge. Our focus is on three categories of knowledge conflicts: context-memory, inter-context, and intra-memory conflict. These conflicts can significantly impact the trustworthiness and performance of LLMs, especially in real-world applications where noise and misinformation are common. By categorizing these conflicts, exploring the causes, examining the behaviors of LLMs under such conflicts, and reviewing available solutions, this survey aims to shed light on strategies for improving the robustness of LLMs, thereby serving as a valuable resource for advancing research in this evolving area.

翻译：本综述深入分析了大语言模型在融合上下文知识与参数化知识时所面临的复杂挑战——知识冲突问题。我们聚焦于三类知识冲突：上下文-记忆冲突、跨上下文冲突与记忆内部冲突。这些冲突会显著影响大语言模型的可信度与性能，尤其在现实应用场景中噪声与错误信息普遍存在的情况下。通过系统分类这三类冲突、探究其成因、分析模型在冲突情境下的行为模式，并梳理现有解决方案，本综述旨在阐明提升大语言模型鲁棒性的策略，从而为该动态研究领域的发展提供重要参考资源。