Recent research has increasingly focused on the extent to which large language models (LLMs) exhibit human-like behavior. In this study, we investigate whether the mental lexicon in LLMs resembles that of humans in terms of lexical organization. Using a word association task—a direct and widely used method for probing word meaning and relationships in the human mind—we evaluated the lexical representations of GPT-4 and Llama-3.1. Our findings reveal that LLMs closely emulate human mental lexicons in capturing semantic relatedness but exhibit notable differences in other properties, such as association frequency and dominant lexical patterns (e.g., top associates). Specifically, LLM lexicons demonstrate greater clustering and reduced diversity compared to the human lexicon, with KL divergence analysis confirming significant deviations in word association patterns. Additionally, LLMs fail to fully capture word association response patterns in different demographic human groups. Among the models, GPT-4 consistently exhibited a slightly higher degree of human-likeness than Llama-3.1. This study highlights both the potential and limitations of LLMs in replicating human mental lexicons, offering valuable insights for applications in natural language processing and cognitive science research involving LLMs.

Large language models segment many words into multiple tokens, and companies that make those models claim that meaningful subword tokens are essential. To investigate whether subword tokens bear meaning, we segmented tens of thousands of words from each of 41 languages according to three generations of GPT tokenizers. We found that words sharing tokens are more semantically similar than expected by chance or expected from length alone, that tokens capture morphological information even when they don’t look like morphemes, and that tokens capture more information than is explained by morphology. In languages that use a script other than the Latin alphabet, GPT-4 tokens are uninformative, but GPT-4o has improved this situation. These results suggest that comparing tokens to morphemes overlooks the wider variety of semantic information available in word form and that standard tokenization methods successfully capture much of that information.