  We use contextualized word definitions generated by large language models as
semantic representations in the task of diachronic lexical semantic change
detection (LSCD). In short, generated definitions are used as `senses', and the
change score of a target word is retrieved by comparing their distributions in
two time periods under comparison. On the material of five datasets and three
languages, we show that generated definitions are indeed specific and general
enough to convey a signal sufficient to rank sets of words by the degree of
their semantic change over time. Our approach is on par with or outperforms
prior non-supervised sense-based LSCD methods. At the same time, it preserves
interpretability and allows to inspect the reasons behind a specific shift in
terms of discrete definitions-as-senses. This is another step in the direction
of explainable semantic change modeling.
