编辑巨擘的心智：深入探究大型语言模型中知识编辑的陷阱
Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models
AI总结：
该综述针对大型语言模型知识编辑的副作用与评估标准不一问题，使用统一指标和基准系统分析现有方法局限，并提出未来研究方向。
摘要：
知识编辑是一种新兴技术，旨在以最小的参数改动高效更新大型语言模型（LLM）中的事实性知识。然而，近期研究发现编辑后会出现副作用，例如知识扭曲和通用能力退化。尽管已有这些发现，对知识编辑陷阱的评估往往依赖不一致的指标和基准，缺乏统一标准。为此，本综述对这些副作用进行了全面研究，通过采用一致的指标和基准开展实验，为LLM中的知识编辑挑战提供了统一视角。此外，我们回顾了相关工作，并概述了解决这些局限的潜在研究方向。本综述强调了当前知识编辑方法的局限性，指出需要更深入地理解LLM的内部知识结构，并改进知识编辑方法。为促进未来研究，我们已在 https://github.com/MiuLab/EditLLM-Survey 公开发布配套材料。
英文摘要：
Knowledge editing is a rising technique for efficiently updating factual knowledge in large language models (LLMs) with minimal alteration of parameters. However, recent studies have identified side effects, such as knowledge distortion and the deterioration of general abilities, that have emerged after editing. Despite these findings, evaluating the pitfalls of knowledge editing often relies on inconsistent metrics and benchmarks, lacking a uniform standard. In response, this survey presents a comprehensive study of these side effects, providing a unified perspective on the challenges of knowledge editing in LLMs by conducting experiments with consistent metrics and benchmarks. Additionally, we review related works and outline potential research directions to address these limitations. Our survey highlights the limitations of current knowledge editing methods, emphasizing the need for a deeper understanding of the inner knowledge structures of LLMs and improved knowledge editing methods. To foster future research, we have released the complementary materials publicly in https://github.com/MiuLab/EditLLM-Survey.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
