  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.
