LLM-generated definitions of IPCC climate terms achieve moderate semantic similarity (0.57 to 0.59) to official definitions and lower readability than the originals.
Word Definitions from Large Language Models
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abstract
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.
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The Accuracy, Robustness, and Readability of LLM-Generated Sustainability-Related Word Definitions
LLM-generated definitions of IPCC climate terms achieve moderate semantic similarity (0.57 to 0.59) to official definitions and lower readability than the originals.