Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/172988
Title: Clause complexing in research-article abstracts: comparing human- and AI-generated texts
Authors: Leong, Alvin Ping
Keywords: Humanities::Language
Issue Date: 2023
Source: Leong, A. P. (2023). Clause complexing in research-article abstracts: comparing human- and AI-generated texts. ExELL, 11(2), 99-132. https://dx.doi.org/10.2478/exell-2023-0008
Journal: ExELL 
Abstract: The ability of chatbots to produce plausible, human-like responses raises questions about the extent of their similarity with original texts. Using a modified version of Halliday’s clause-complexing framework, this study compared 50 abstracts of scientific research articles from Nature with generated versions produced by Bard, ChatGPT, and Poe Assistant. None of the chatbots matched the original abstracts in all categories. The only chatbot that came closest was ChatGPT, but differences in the use of finite adverbial clauses and –ing elaborating clauses were detected. Incorporating distinct grammatical features in the algorithms of AI-detection tools is crucially needed to enhance the reliability of their results. A genre-based approach to detecting AI-generated content is recommended.
URI: https://hdl.handle.net/10356/172988
ISSN: 2303-4858
DOI: 10.2478/exell-2023-0008
Schools: School of Humanities 
Rights: © The Author. This is an open-access article distributed under the terms of the Creative Commons License.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SoH Journal Articles

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