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：近期，基于大语言模型的代表性模型ChatGPT因其强大的涌现能力而备受关注。部分研究者认为大语言模型（LLMs）可能替代知识图谱等结构化知识库，充当参数化知识库。然而，尽管大语言模型善于从海量语料中学习概率语言模式并与人类进行对话，但与前代较小的预训练语言模型（PLMs）类似，它们在生成基于知识的内容时仍难以准确回忆事实。为突破这一局限，研究者提出通过引入基于知识图谱的知识增强数据驱动的预训练语言模型，将显式事实知识融入其中，从而提升其在需要事实知识的文本生成任务中的表现，并为用户查询提供更具信息量的回答。本文综述了知识图谱增强预训练语言模型（KGPLMs）的相关研究，详细阐述了现有KGPLM模型及其应用。受已有KGPLM研究的启发，本文提出构建知识图谱增强大语言模型（KGLLMs）以整合知识图谱与大语言模型。KGLLM为大语言模型的事实推理能力增强提供了解决方案，为大语言模型研究开辟了新方向。