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Large language models (LLMs) exhibit superior performance on various naturallanguage tasks, but they are susceptible to issues stemming from outdated dataand domain-specific limitations. In order to address these challenges,researchers have pursued two primary strategies, knowledge editing andretrieval augmentation, to enhance LLMs by incorporating external informationfrom different aspects. Nevertheless, there is still a notable absence of acomprehensive survey. In this paper, we propose a review to discuss the trendsin integration of knowledge and large language models, including taxonomy ofmethods, benchmarks, and applications. In addition, we conduct an in-depthanalysis of different methods and point out potential research directions inthe future. We hope this survey offers the community quick access and acomprehensive overview of this research area, with the intention of inspiringfuture research endeavors.

Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications