Large language models (LLMs) have achieved impressive advancements across numerous disciplines, yet the critical issue of knowledge conflicts, a major source of hallucinations, has rarely been studied. Only a few research explored the conflicts between the inherent knowledge of LLMs and the retrieved contextual knowledge. However, a thorough assessment of knowledge conflict in LLMs is still missing. Motivated by this research gap, we present ConflictBank, the first comprehensive benchmark developed to systematically evaluate knowledge conflicts from three aspects: (i) conflicts encountered in retrieved knowledge, (ii) conflicts within the models' encoded knowledge, and (iii) the interplay between these conflict forms. Our investigation delves into four model families and twelve LLM instances, meticulously analyzing conflicts stemming from misinformation, temporal discrepancies, and semantic divergences. Based on our proposed novel construction framework, we create 7,453,853 claim-evidence pairs and 553,117 QA pairs. We present numerous findings on model scale, conflict causes, and conflict types. We hope our ConflictBank benchmark will help the community better understand model behavior in conflicts and develop more reliable LLMs.

翻译：大语言模型（LLMs）已在众多学科领域取得了令人瞩目的进展，然而，作为幻觉主要来源之一的知识冲突这一关键问题却鲜有研究。仅有少数研究探讨了LLMs固有知识与检索到的上下文知识之间的冲突。然而，目前仍缺乏对LLMs中知识冲突的全面评估。受此研究空白启发，我们提出了ConflictBank，这是首个为系统评估知识冲突而开发的综合性基准，涵盖三个方面：（i）检索知识中遇到的冲突，（ii）模型编码知识内部的冲突，以及（iii）这些冲突形式之间的相互作用。我们的研究深入探究了四个模型家族和十二个LLM实例，细致分析了源于错误信息、时间差异和语义分歧的冲突。基于我们提出的新颖构建框架，我们创建了7,453,853个声明-证据对和553,117个问答对。我们展示了关于模型规模、冲突成因及冲突类型的众多发现。我们希望我们的ConflictBank基准能够帮助社区更好地理解模型在冲突中的行为，并开发出更可靠的LLMs。