Human-likeness of LLMs in the Mental Lexicon

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.