Contextual assembly of lexical functions in large language models.

ABSTRACT: Neural network modeling has played a central role in psycholinguistic studies of lexical processing, but the recent advent of large language models (LLMs) offers a different approach that may yield new insights into the mental lexicon. Four LLMs were prompted across three experiments to test how they generate psycholinguistic ratings of words in comparison with humans. LLM ratings, averaged across varying list contexts, were found to be highly correlated with human ratings, and differences in correlation strengths were partly explained by differences in rating ambiguity. LLM context manipulations strengthened correlations with human ratings through better calibration, and variability in LLM ratings was correlated with human inter-rater variability. Additional results from testing LLM gener

SUBMITTER: Kello CT 

PROVIDER: S-EPMC12686107 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature