  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.
