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

Abstract

The degree to which large language models (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 (ChatGPT-3.5, ChatGPT-4, ChatGPT-o4 mini, and ChatGPT-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 ChatGPT-generated texts differed significantly from human-written texts for each variable, with ChatGPT-o4 mini and ChatGPT-4.5 differing the most. Within these two groups, ChatGPT-4.5 demonstrated higher levels of lexical diversity than older models despite producing fewer tokens. The human writers' lexical diversity did not differ across subgroups (i.e., education, language status). Altogether, the results indicate that ChatGPT models do not produce human-like texts in relation to lexical diversity, and the newer models produce less human-like text than older models. We discuss the implications of these results for language pedagogy and related applications.