Large Language Models on Lexical Semantic Change Detection: An Evaluation

TL;DR

This paper evaluates the effectiveness of large language models in detecting lexical semantic change, introducing novel prompting methods and comprehensive assessments across multiple model generations.

Contribution

It is the first to systematically evaluate LLMs for lexical semantic change detection and proposes new prompting techniques for this task.

Findings

LLMs show potential in semantic change detection

Novel prompting strategies improve detection accuracy

Comprehensive evaluation across model generations

Abstract

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.