This paper provides a comprehensive study of knowledge editing for Large Language Models (LLMs), addressing the challenges of computational demands and the need for frequent updates to ensure their relevance. The authors define the knowledge editing problem and review cutting-edge approaches, categorizing them into three groups: resorting to external knowledge, merging knowledge into the model, and editing intrinsic knowledge. They introduce a new benchmark, KnowEdit, for empirical evaluation and analyze the effectiveness of different knowledge editing methods. The paper also explores the impact of knowledge editing on general tasks and multi-task learning, providing insights into the underlying knowledge structures within LLMs. Additionally, it discusses potential applications of knowledge editing, including efficient machine learning, AI-generated content, trustworthy AI, and human-computer interaction. The authors aim to facilitate future research by releasing an open-source framework, EasyEdit, and highlight the broader implications of knowledge editing techniques, such as energy consumption and interpretability.This paper provides a comprehensive study of knowledge editing for Large Language Models (LLMs), addressing the challenges of computational demands and the need for frequent updates to ensure their relevance. The authors define the knowledge editing problem and review cutting-edge approaches, categorizing them into three groups: resorting to external knowledge, merging knowledge into the model, and editing intrinsic knowledge. They introduce a new benchmark, KnowEdit, for empirical evaluation and analyze the effectiveness of different knowledge editing methods. The paper also explores the impact of knowledge editing on general tasks and multi-task learning, providing insights into the underlying knowledge structures within LLMs. Additionally, it discusses potential applications of knowledge editing, including efficient machine learning, AI-generated content, trustworthy AI, and human-computer interaction. The authors aim to facilitate future research by releasing an open-source framework, EasyEdit, and highlight the broader implications of knowledge editing techniques, such as energy consumption and interpretability.