Lexical Semantic Change Detection stands out as one of the few areas where Large Language Models (LLMs) have not been extensively involved. Traditional methods like PPMI, and SGNS remain prevalent in research, alongside newer BERT-based approaches. Despite the comprehensive coverage of various natural language processing domains by LLMs, there is a notable scarcity of literature concerning their application in this specific realm. In this work, we seek to bridge this gap by introducing LLMs into the domain of Lexical Semantic Change Detection. Our work presents novel prompting solutions and a comprehensive evaluation that spans all three generations of language models, contributing to the exploration of LLMs in this research area.

翻译：词汇语义变化检测是少数尚未广泛涉及大型语言模型（LLMs）的领域之一。传统方法如PPMI和SGNS在研究领域中仍占主导地位，同时伴随基于BERT的新方法。尽管LLMs已全面覆盖各种自然语言处理领域，但关于其在特定领域应用的文献仍显著匮乏。本研究通过将LLMs引入词汇语义变化检测领域，旨在填补这一空白。我们提出了创新的提示解决方案，并开展了涵盖三代语言模型的全面评估，为LLMs在该研究领域的探索做出了贡献。