The advancement of Large Language Models (LLMs) has revolutionized natural language processing, yet their training on massive corpora poses significant risks, including the memorization of sensitive personal data, copyrighted material, and knowledge that could facilitate malicious activities. To mitigate these issues and align with legal and ethical standards such as the "right to be forgotten", machine unlearning has emerged as a critical technique to selectively erase specific knowledge from LLMs without compromising their overall performance. This survey provides a systematic review of over 180 papers on LLM unlearning published since 2021, focusing exclusively on large-scale generative models. Distinct from prior surveys, we introduce novel taxonomies for both unlearning methods and evaluations. We clearly categorize methods into training-time, post-training, and inference-time based on the training stage at which unlearning is applied. For evaluations, we not only systematically compile existing datasets and metrics but also critically analyze their advantages, disadvantages, and applicability, providing practical guidance to the research community. In addition, we discuss key challenges and promising future research directions. Our comprehensive overview aims to inform and guide the ongoing development of secure and reliable LLMs.

翻译：大语言模型（LLMs）的进步彻底改变了自然语言处理领域，然而，其基于海量语料库的训练也带来了显著风险，包括对敏感个人数据、受版权保护材料以及可能助长恶意活动的知识的记忆。为缓解这些问题并符合“被遗忘权”等法律与伦理标准，机器遗忘技术已成为一项关键技术，旨在选择性地从LLMs中擦除特定知识，同时不损害其整体性能。本文对2021年以来发表的180余篇关于LLM遗忘的论文进行了系统性综述，并聚焦于大规模生成模型。与以往综述不同，我们针对遗忘方法和评估体系提出了新的分类框架。我们根据遗忘技术应用的训练阶段，将方法清晰划分为训练时、训练后和推理时三类。在评估方面，我们不仅系统性地梳理了现有数据集与评价指标，还批判性地分析了其优缺点及适用性，为研究社区提供了实用指导。此外，我们探讨了关键挑战与未来有前景的研究方向。本综述旨在为安全可靠的大语言模型的持续发展提供参考与指引。