  Dictionary definitions are historically the arbitrator of what words mean,
but this primacy has come under threat by recent progress in NLP, including
word embeddings and generative models like ChatGPT. We present an exploratory
study of the degree of alignment between word definitions from classical
dictionaries and these newer computational artifacts. Specifically, we compare
definitions from three published dictionaries to those generated from variants
of ChatGPT. We show that (i) definitions from different traditional
dictionaries exhibit more surface form similarity than do model-generated
definitions, (ii) that the ChatGPT definitions are highly accurate, comparable
to traditional dictionaries, and (iii) ChatGPT-based embedding definitions
retain their accuracy even on low frequency words, much better than GloVE and
FastText word embeddings.
