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(LLMs) exhibit superior performance on various natural language tasks, but they are susceptible to issues stemming from outdated data and domain-specific limitations.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）在各类自然语言任务中展现出卓越性能，但受限于数据时效性不足与领域特异性问题。为应对这些挑战，研究者主要采取知识编辑与检索增强两种策略，通过整合不同维度的外部信息来增强LLMs。然而，目前仍缺乏系统性的综述研究。本文提出一项综述，旨在探讨知识与大语言模型融合的发展趋势，涵盖方法分类学、基准测试及应用场景。此外，我们对不同方法进行了深入分析，并指出未来潜在的研究方向。我们期望本综述能为该研究领域提供快速入门指南与全景式概览，从而启迪后续研究工作。