“It is important to consult” a linguist: Verb-Argument Constructions in ChatGPT and human experts’ medical and financial advice

This paper adopts a Usage-Based Construction Grammar perspective to compare human- and AI-generated language, focusing on Verb-Argument Constructions (VACs) as a lens for analysis. Specifically, we examine solicited advice texts in two domains-Finance and Medicine-produced by humans and ChatGPT across different GPT models (3.5, 4, and 4o) and interfaces (3.5 Web vs. 3.5 API). Our findings reveal broad consistency in the frequency and distribution of the most common VACs across human- and AI-generated texts, though ChatGPT exhibits a slightly higher reliance on the most frequent constructions. A closer examination of the verbs occupying these constructions uncovers significant differences in the meanings conveyed, with a notable growth away from human-like language production in macro level perspectives (e.g., length) and towards humanlike verb-VAC patterns with newer models. These results underscore the potential of VACs as a powerful tool for analyzing AI-generated language and tracking its evolution over time.

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Journal
PLoS ONE
Published
2025-05-27
DOI
https://doi.org/10.1371/journal.pone.0324611
Primary Topic
Natural Language Processing Techniques
Type
article
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article

“It is important to consult” a linguist: Verb-Argument Constructions in ChatGPT and human experts’ medical and financial advice

Christopher Stewart, J. Elliott Casal, Alistair Windsor
1 links
PLoS ONE
Natural Language Processing Techniques
article

“It is important to consult” a linguist: Verb-Argument Constructions in ChatGPT and human experts’ medical and financial advice

Christopher Stewart, J. Elliott Casal, Alistair Windsor
article en

Abstract

This paper adopts a Usage-Based Construction Grammar perspective to compare human- and AI-generated language, focusing on Verb-Argument Constructions (VACs) as a lens for analysis. Specifically, we examine solicited advice texts in two domains-Finance and Medicine-produced by humans and ChatGPT across different GPT models (3.5, 4, and 4o) and interfaces (3.5 Web vs. 3.5 API). Our findings reveal broad consistency in the frequency and distribution of the most common VACs across human- and AI-generated texts, though ChatGPT exhibits a slightly higher reliance on the most frequent constructions. A closer examination of the verbs occupying these constructions uncovers significant differences in the meanings conveyed, with a notable growth away from human-like language production in macro level perspectives (e.g., length) and towards humanlike verb-VAC patterns with newer models. These results underscore the potential of VACs as a powerful tool for analyzing AI-generated language and tracking its evolution over time.

PLoS ONEVol. 20(5)
University of Memphis (US)
Quality Education
Openalex Percentile: Top 95%
Natural Language Processing Techniques
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