Do LLMs produce texts with "human-like" lexical diversity?

Abstract: The degree to which LLMs produce writing that is truly human-like remains unclear despite the extensive empirical attention that this question has received. The present study addresses this question from the perspective of lexical diversity. Specifically, the study investigates patterns of lexical diversity in LLM-generated texts from four ChatGPT models (-3.5, -4, -o4 mini, and -4.5) in comparison with texts written by L1 and L2 English participants (n = 240) across four education levels. Six dimensions of lexical diversity were measured in each text: volume, abundance, variety-repetition, evenness, disparity, and dispersion. Results from one-way MANOVAs, one-way ANOVAS, and Support Vector Machines revealed that the LLM-generated texts differed significantly from human-written texts for each variable, with ChatGPT-o4 mini and -4.5 differing the most. Within these two groups, ChatGPT-4.5 demonstrated higher levels of lexical diversity despite producing fewer tokens. The human writers' lexical diversity did not differ across subgroups (i.e., education, language status). Altogether, the results indicate that LLMs do not produce human-like texts in relation to lexical diversity, and the newer LLMs produce less human-like texts than older models. We discuss the implications of these results for language pedagogy and related applications.