Recently, ChatGPT, a representative large language model (LLM), has gained considerable attention due to its powerful emergent abilities. Some researchers suggest that LLMs could potentially replace structured knowledge bases like knowledge graphs (KGs) and function as parameterized knowledge bases. However, while LLMs are proficient at learning probabilistic language patterns based on large corpus and engaging in conversations with humans, they, like previous smaller pre-trained language models (PLMs), still have difficulty in recalling facts while generating knowledge-grounded contents. To overcome these limitations, researchers have proposed enhancing data-driven PLMs with knowledge-based KGs to incorporate explicit factual knowledge into PLMs, thus improving their performance to generate texts requiring factual knowledge and providing more informed responses to user queries. This paper reviews the studies on enhancing PLMs with KGs, detailing existing knowledge graph enhanced pre-trained language models (KGPLMs) as well as their applications. Inspired by existing studies on KGPLM, this paper proposes to enhance LLMs with KGs by developing knowledge graph-enhanced large language models (KGLLMs). KGLLM provides a solution to enhance LLMs' factual reasoning ability, opening up new avenues for LLM research.

翻译：最近，以ChatGPT为代表的大型语言模型（LLM）因其强大的涌现能力而受到广泛关注。一些研究者认为，LLM可能取代知识图谱（KG）等结构化知识库，充当参数化知识库的角色。然而，尽管LLM擅长基于大规模语料库学习概率语言模式并与人类进行对话，但它们与先前较小的预训练语言模型（PLM）一样，在生成需要知识支撑的内容时仍难以准确回忆事实。为克服这一局限，研究者提出用基于知识的KG增强数据驱动的PLM，将显式事实知识融入PLM，从而提升其生成需事实知识的文本的能力，并为用户查询提供更准确的回应。本文综述了利用KG增强PLM的研究，详细介绍了现有知识图谱增强的预训练语言模型（KGPLM）及其应用。受现有KGPLM研究的启发，本文提出通过开发知识图谱增强的大语言模型（KGLLM）来增强LLM。KGLLM为提升LLM的事实推理能力提供了解决方案，为LLM研究开辟了新途径。