Monolingual and bilingual speakers of the same languages derive unique meanings for words, partly based on between-language differences in meaning of dictionary-translated words. Do language models also capture this variability between speakers? We compared several models of lexical semantic representation and their correspondences to a word-word meaning similarity rating task done by both L1 and L2 English speakers. We found most language models do not differently correlate with L1 vs. L2 English speakers. Further, these models exhibit more cross-language similarity between Mandarin and English representations than is supported by psycholinguistic research. Only GloVe and OpenAI’s Davinci models more strongly correlated with L1 speakers than L2, but individual participants’ similarity to these models did not relate to language history variables that might otherwise predict bilingual lexical semantic native-likeness. We concluded that language models are not yet reliable references for tracking lexical semantic learning and discuss future directions for computational and psycholinguistics.

How do essentialist beliefs about categories arise? We hypothesize that such beliefs are transmitted via language. We subject large language models (LLMs) to vignettes from the literature on essentialist categorization and find that they align well with people when the studies manipulated teleological information – information about what something is for. We examine whether in a classic test of essentialist categorization – the transformation task – LLMs prioritize teleological properties over information about what something looks like, or is made of. Experiments 1 and 2 find that telos and what something is made of matter more than appearance. Experiment 3 manipulates all three factors and finds that what something is for matters more than what it’s made of. Overall, these studies suggest that language alone may be sufficient to give rise to essentialist beliefs, and that information about what something is for matters more.