Incorporating factual knowledge in knowledge graph is regarded as a promising approach for mitigating the hallucination of large language models (LLMs). Existing methods usually only use the user's input to query the knowledge graph, thus failing to address the factual hallucination generated by LLMs during its reasoning process. To address this problem, this paper proposes Knowledge Graph-based Retrofitting (KGR), a new framework that incorporates LLMs with KGs to mitigate factual hallucination during the reasoning process by retrofitting the initial draft responses of LLMs based on the factual knowledge stored in KGs. Specifically, KGR leverages LLMs to extract, select, validate, and retrofit factual statements within the model-generated responses, which enables an autonomous knowledge verifying and refining procedure without any additional manual efforts. Experiments show that KGR can significantly improve the performance of LLMs on factual QA benchmarks especially when involving complex reasoning processes, which demonstrates the necessity and effectiveness of KGR in mitigating hallucination and enhancing the reliability of LLMs.

翻译：将知识图谱中的事实性知识融入大语言模型被视为缓解其幻觉问题的重要途径。现有方法通常仅利用用户输入查询知识图谱，因而无法处理模型在推理过程中产生的事实性幻觉。针对该问题，本文提出知识图谱回溯修正框架（KGR），该框架通过将大语言模型与知识图谱结合，基于知识图谱中存储的事实性知识对模型初始生成结果进行回溯修正，从而在推理过程中缓解事实性幻觉。具体而言，KGR利用大语言模型对模型生成文本中的事实性陈述进行提取、筛选、验证和回溯修正，从而构建无需人工干预的自主知识验证与精炼流程。实验表明，KGR能显著提升大语言模型在事实性问答基准上的表现，尤其在涉及复杂推理过程的任务中效果更为突出，这充分证明了KGR在缓解幻觉、增强大语言模型可靠性方面的必要性与有效性。