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

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 captured by core vocabulary measures. Across two experiments, 867 participants completed a task which involved guessing target words that were missing from sentence contexts. Word frequency-based core words were easier to guess overall, but when the predictability of words was taken into account, word association- and acquisition-based core words were easier to predict, indicating that people have a preference for simpler or more basic words over and above distributional predictability. Additionally, we show that there are systematic deviations between the predictions of a large language model (BERT) and humans, and that these are best captured by word association-based coreness. The findings suggest that distributional relationships between words in text is not all there is to human word prediction, but that people may also rely on factors like communicative usefulness and multimodal or extralinguistic information. Implications for core vocabulary, word prediction, and the relationship between large language models and human cognition are discussed.

Age of Acquisition · basic vocabulary · Distributional semantics · language learning · large language models · sentence completion · Word Association · word frequency · word prediction · Language Development and Disorders · Natural Language Processing Techniques · Reading and Literacy Development