This paper presents a comprehensive study of knowledge editing for large language models (LLMs), focusing on methods to efficiently modify LLMs' behaviors while preserving overall performance. The authors define the knowledge editing problem and provide a detailed review of recent 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 evaluating knowledge editing techniques and analyze the underlying knowledge structures within LLMs. The paper also discusses the broader implications of knowledge editing, including its potential applications in efficient machine learning, AI-generated content, trustworthy AI, and human-computer interaction. The authors propose an open-source framework, EasyEdit, to facilitate future research in this area. The study highlights the importance of knowledge editing in addressing the limitations of LLMs, such as factual inaccuracies and outdated information, while ensuring the models remain adaptable and reliable. The paper emphasizes the need for efficient and targeted modifications to LLMs, enabling them to better align with real-world knowledge and applications. The authors also discuss the challenges and considerations involved in knowledge editing, including the potential for unintended consequences and the need for careful implementation. Overall, the paper aims to advance the understanding and development of knowledge editing techniques for LLMs, contributing to the broader goal of creating more accurate, adaptable, and trustworthy AI systems.This paper presents a comprehensive study of knowledge editing for large language models (LLMs), focusing on methods to efficiently modify LLMs' behaviors while preserving overall performance. The authors define the knowledge editing problem and provide a detailed review of recent 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 evaluating knowledge editing techniques and analyze the underlying knowledge structures within LLMs. The paper also discusses the broader implications of knowledge editing, including its potential applications in efficient machine learning, AI-generated content, trustworthy AI, and human-computer interaction. The authors propose an open-source framework, EasyEdit, to facilitate future research in this area. The study highlights the importance of knowledge editing in addressing the limitations of LLMs, such as factual inaccuracies and outdated information, while ensuring the models remain adaptable and reliable. The paper emphasizes the need for efficient and targeted modifications to LLMs, enabling them to better align with real-world knowledge and applications. The authors also discuss the challenges and considerations involved in knowledge editing, including the potential for unintended consequences and the need for careful implementation. Overall, the paper aims to advance the understanding and development of knowledge editing techniques for LLMs, contributing to the broader goal of creating more accurate, adaptable, and trustworthy AI systems.