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(LLMs) exhibit superior performance on various natural language tasks, but they are susceptible to issues stemming from outdated data and domain-specific limitations.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

翻译：大语言模型（LLMs）在各种自然语言任务中表现出卓越性能，但易受数据过时和领域局限性等问题的困扰。为解决这些挑战，研究者主要采用知识编辑和检索增强两种策略，通过整合不同维度的外部信息来增强大语言模型。然而，目前仍缺乏全面系统的综述研究。本文提出一篇综述，探讨知识与大语言模型的融合趋势，涵盖方法分类、基准测试及应用场景。此外，我们深入分析不同方法，并指出未来潜在的研究方向。希望本综述能够为该领域研究者提供快速入门途径和全面研究概览，以启发后续研究工作。