Large Language Models on Lexical Semantic Change Detection: An
  Evaluation
Ruiyu Wang Matthew Choi
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.