Languages continually evolve in response to societal events, resulting in new terms and shifts in meanings. These changes have significant implications for computer applications, including automatic translation and chatbots, making it essential to characterize them accurately. The recent development of LLMs has notably advanced natural language understanding, particularly in sense inference and reasoning. In this paper, we investigate the potential of LLMs in characterizing three types of semantic change: dimension, relation, and orientation. We achieve this by combining LLMs' Chain-of-Thought with rhetorical devices and conducting an experimental assessment of our approach using newly created datasets. Our results highlight the effectiveness of LLMs in capturing and analyzing semantic changes, providing valuable insights to improve computational linguistic applications.

本研究针对语言中由于社会事件而产生的语义变化问题，提出了一种新的方法，通过结合大型语言模型（LLMs）的思维链和修辞手法来分析语义变化的三个类型：维度、关系和方向。研究结果表明，该方法能够有效捕捉和分析语义变化，为改善计算语言学应用提供了重要的见解。

使用修辞学的LLMs进行语义变化表征