Do LLMs produce texts with "human-like" lexical diversity?
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Semantic Delta: An Interpretable Signal Differentiating Human and LLMs Dialogue
Do LLMs talk like us? This question intrigues a multitude of scholar and it is relevant in many fields, from education to academia. This work presents an interpretable statistical feature for distinguishing human written…
AI Writers Have a Consistent Stylometric Footprint, but AI Editors Do Not
Text generated by large language models (LLMs) has been shown to be stylometrically distinct from human-written text \citep{andreDetectingAIAuthorship2023, shahDetectingUnmaskingAIGenerated2023, oparaStyloAIDistinguishin…
Your Language Model Can Secretly Write Like Humans: Contrastive Paraphrase Attacks on LLM-Generated Text Detectors
The misuse of large language models (LLMs), such as academic plagiarism, has driven the development of detectors to identify LLM-generated texts. To bypass these detectors, paraphrase attacks have emerged to purposely re…
Language ModelingLanguage ModellingThe Astonishing Ability of Large Language Models to Parse Jabberwockified Language
We show that large language models (LLMs) have an astonishing ability to recover meaning from severely degraded English texts. Texts in which content words have been randomly substituted by nonsense strings, e.g., "At th…
Poor Alignment and Steerability of Large Language Models: Evidence from College Admission Essays
People are increasingly using technologies equipped with large language models (LLM) to write texts for formal communication, which raises two important questions at the intersection of technology and society: Who do LLM…