探索知识冲突以实现忠实的LLM推理：基准与方法
Exploring Knowledge Conflicts for Faithful LLM Reasoning: Benchmark and Method
AI总结：
本文提出ConflictQA基准，揭示LLM在处理跨源知识冲突时的不足，并提出XoT框架以提升异构冲突证据推理的可靠性。
摘要：
大型语言模型（LLMs）在广泛的应用中通过检索增强生成（RAG）技术取得了显著成功。然而，当检索到冲突知识时，LLMs在忠实推理方面表现不佳。现有研究主要关注外部知识与LLM参数知识之间的冲突，忽略了外部知识之间的冲突。现代RAG系统越来越强调整合非结构化文本和（半）结构化数据如知识图谱（KGs）以提高知识完整性和推理忠实性。为填补这一空白，我们引入ConflictQA，一个新颖的基准，系统地实例化文本证据与KG证据之间的冲突。在代表性LLMs上的广泛评估表明，面对此类跨源冲突，LLMs往往无法识别可靠证据进行正确推理。相反，LLMs对提示选择更敏感，倾向于仅依赖KG或文本证据，导致错误响应。基于这些发现，我们进一步提出XoT，一种针对异构冲突证据推理的两阶段解释性思维框架，并通过大量实验验证其有效性。
英文摘要：
Large language models (LLMs) have achieved remarkable success across a wide range of applications especially when augmented by external knowledge through retrieval-augmented generation (RAG). Despite their widespread adoption, recent studies have shown that LLMs often struggle to perform faithful reasoning when conflicting knowledge is retrieved. However, existing work primarily focuses on conflicts between external knowledge and the parametric knowledge of LLMs, leaving conflicts across external knowledge largely unexplored. Meanwhile, modern RAG systems increasingly emphasize the integration of unstructured text and (semi-)structured data like knowledge graphs (KGs) to improve knowledge completeness and reasoning faithfulness. To address this gap, we introduce ConflictQA, a novel benchmark that systematically instantiates conflicts between textual evidence and KG evidence. Extensive evaluations across representative LLMs reveal that, facing such cross-source conflicts, LLMs often fail to identify reliable evidence for correct reasoning. Instead, LLMs become more sensitive to prompting choices and tend to rely exclusively on either KG or textual evidence, resulting in incorrect responses. Based on these findings, we further propose XoT, a two-stage explanation-based thinking framework tailored for reasoning over heterogeneous conflicting evidence, and verify its effectiveness with extensive experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
