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.

翻译：词典定义历来是词语含义的裁决者，但这一首要地位正受到自然语言处理最新进展的威胁，包括词嵌入和像ChatGPT这样的生成模型。我们开展了一项探索性研究，旨在考察经典词典中的词语定义与这些新兴计算产物之间的对齐程度。具体而言，我们将三部已出版词典的定义与ChatGPT变体生成的定义进行比较。研究表明：(i) 不同传统词典的定义在表面形式上比模型生成的定义具有更高的相似性；(ii) ChatGPT生成的定义具有极高的准确性，可与传统词典相媲美；(iii) 基于ChatGPT的嵌入定义即使在低频词上也能保持其准确性，其表现远优于GloVE和FastText词嵌入。