Mitigating the hallucinations of Large Language Models (LLMs) and enhancing them is a crucial task. Although some existing methods employ model self-enhancement techniques, they fall short of effectively addressing unknown factual hallucinations. Using Knowledge Graph (KG) enhancement approaches fails to address the generalization across different KG sources and the enhancement of open-ended answer questions simultaneously. To tackle these limitations, there is a framework that combines Pseudo-Graph Generation and Atomic Knowledge Verification proposed. The enhancement of LLM using KG in an open-ended question-answering setting is implemented by leveraging the Pseudo-Graph Generation. Atomic Knowledge Verification utilizes atomic-level knowledge querying and verification to achieve generalizability under different KG sources. Compared to the baseline, this approach yields a minimum improvement of 11.5 in the ROUGE-L score for open-ended questions. For precise questions, we observe a minimum accuracy improvement of 7.5. Moreover, there is also demonstration that this framework exhibits generalizability across different KG sources. In summary, our results pave the way for enhancing LLMs by incorporating Pseudo- and Multisource-KGs, particularly in the context of open-ended questions.

翻译：缓解大型语言模型（LLM）的幻觉现象并增强其性能是一项关键任务。尽管现有方法采用了模型自我增强技术，但仍未能有效解决未知事实性幻觉。基于知识图谱（KG）的增强方法无法同时实现跨不同知识图谱源的泛化能力和对开放式答案问题的增强。为解决这些局限，本文提出了一种结合伪图谱生成与原子知识验证的框架。通过利用伪图谱生成，实现了在开放式问答场景中基于知识图谱增强大型语言模型的效果。原子知识验证通过原子级知识查询与验证，实现了不同知识图谱源下的泛化能力。与基线方法相比，本方法在开放式问题的ROUGE-L评分上至少提升11.5分；对于精确问题，准确率至少提升7.5%。此外，实验证明该框架在不同知识图谱源上均表现出泛化能力。综上所述，我们的研究结果为通过融入伪知识图谱与多源知识图谱增强大型语言模型（尤其在开放式问答场景中）开辟了新路径。