Lexical Semantic Change (LSC) is the task of identifying, interpreting, and assessing the possible change over time in the meanings of a target word. Traditionally, LSC has been addressed by linguists and social scientists through manual and time-consuming analyses, which have thus been limited in terms of the volume, genres, and time-frame that can be considered. In recent years, computational approaches based on Natural Language Processing have gained increasing attention to automate LSC as much as possible. Significant advancements have been made by relying on Large Language Models (LLMs), which can handle the multiple usages of the words and better capture the related semantic change. In this article, we survey the approaches based on LLMs for LSC and we propose a classification framework characterized by three dimensions: meaning representation, time-awareness, and learning modality. The framework is exploited to i) review the measures for change assessment, ii) compare the approaches on performance, and iii) discuss the current issues in terms of scalability, interpretability, and robustness. Open challenges and future research directions about the use of LLMs for LSC are finally outlined.

词汇语义变化 (LSC) 是识别、解释和评估目标词的含义随时间可能发生的变化的任务。传统上，LSC 是由语言学家和社会科学家通过手动和耗时的分析来解决的，因此在可以考虑的数量、类型和时间框架方面受到限制。近年来，基于自然语言处理的计算方法越来越受到人们的关注，以尽可能地实现 LSC 的自动化。依靠大型语言模型（LLMs）已经取得了重大进展，它可以处理单词的多种用法并更好地捕获相关的语义变化。在本文中，我们调查了基于LLMs的LSC方法，并提出了一个以三个维度为特征的分类框架：意义表示、时间意识和学习模态。该框架用于 i) 审查变革评估的措施，ii) 比较绩效方法，以及 iii) 讨论可扩展性、可解释性和稳健性方面的当前问题。最后概述了关于 LSC 使用 LLMs 的开放挑战和未来研究方向。