Lexical organization in humans and large language models: Evidence from L1, L2, and LLM networks

This study compares paradigmatic lexical networks in native speakers of Spanish (L1), advanced learners of Spanish as a foreign language (L2), and a large language model (ChatGPT 5.2) in order to examine how the organization of model-generated networks compares to that observed in human speakers. All groups produced vocabulary appropriate to the target category and displayed coherent local clustering. However, the two human groups showed highly similar global network structures despite differences in acquisition history, whereas the model exhibited higher modularity, longer path lengths, and substantially higher lexical convergence. L2 learners, despite more limited exposure to Spanish, converged with the L1 group on all key structural measures, while the model diverged from both. In addition, the relation between lexical availability and familiarity observed in human speakers was not present in the model. These results indicate that similarities in lexical content do not entail comparable organization, and suggest that human lexical systems converge on shared structural properties under different learning conditions. The findings also highlight the value of including speakers at different stages of acquisition in comparisons between human and artificial language systems.