With the implementation of personal data privacy regulations, the field of machine learning (ML) faces the challenge of the "right to be forgotten". Machine unlearning has emerged to address this issue, aiming to delete data and reduce its impact on models according to user requests. Despite the widespread interest in machine unlearning, comprehensive surveys on its latest advancements, especially in the field of Large Language Models (LLMs) is lacking. This survey aims to fill this gap by providing an in-depth exploration of machine unlearning, including the definition, classification and evaluation criteria, as well as challenges in different environments and their solutions. Specifically, this paper categorizes and investigates unlearning on both traditional models and LLMs, and proposes methods for evaluating the effectiveness and efficiency of unlearning, and standards for performance measurement. This paper reveals the limitations of current unlearning techniques and emphasizes the importance of a comprehensive unlearning evaluation to avoid arbitrary forgetting. This survey not only summarizes the key concepts of unlearning technology but also points out its prominent issues and feasible directions for future research, providing valuable guidance for scholars in the field.

通过提供深入探讨机器消遣技术的定义、分类和评价标准，以及不同环境下的挑战和解决方案，本文对传统模型和大型语言模型上的消遣进行分类和研究，提出了评估消遣效果和效率的方法以及性能测量标准。本文揭示了当前消遣技术的局限性，并强调了全面的消遣评估的重要性，以避免随意的遗忘。该调查不仅总结了消遣技术的关键概念，还指出了其突出问题和未来研究的可行方向，为该领域的学者提供了有价值的指导。

传统模型与大型语言模型的机器遗忘：简要调查