Core Vocabulary Reveals Differences Between Human Word Prediction and Large Language Models

Abstract

The question of which words are the most central or important to a language has been explored in various ways. In this study, we propose definitions of core vocabulary that are based on how language is learned, represented, and processed from psychological perspectives, and test these on a word prediction task. We aim to (1) compare core vocabulary based on word frequency in natural language, word association network centrality, and age-of-acquisition in terms of how well they are guessed in word prediction contexts, and (2) investigate the extent to which word prediction in large language models aligns with humans, and if there are systematic differences between them, whether these can be c..