Plagiarism in the age of massive generative pre-trained transformers (GPT-3)
Open accessAbstract
As if 2020 was not a peculiar enough year, its fifth month saw the relatively quiet publication of a preprint describing the most powerful natural language processing (NLP) system to date—GPT-3 (Generative Pre-trained Transformer-3)—created by the Silicon Valley research firm OpenAI. Though the software implementation of GPT-3 is still in its initial beta release phase, and its full capabilities are still unknown as of the time of this writing, it has been shown that this artificial intelligence can comprehend prompts in natural language, on virtually any topic, and generate relevant original text content that is indistinguishable from human writing. Moreover, access to these capabilities, in a limited yet worrisome enough extent, is available to the general public. This paper presents examples of original content generated by the author using GPT-3. These examples illustrate some of the capabilities of GPT-3 in comprehending prompts in natural language and generating convincing content in response. I use these examples to raise specific fundamental questions pertaining to the intellectual property of this content and the potential use of GPT-3 to facilitate plagiarism. The goal is to instigate a sense of urgency, as well as a sense of present tardiness on the part of the academic community in addressing these questions.
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Students could potentially use AI essay-writing systems to cheat on their assignments by submitting essays that are not their own work.
“This means that students could potentially use these systems to cheat on their assignments by submitting essays that are not their own work (e.g.Dehouche, 2021).”
From Chatting and cheating: Ensuring academic integrity in the era of ChatGPT (2024) · cites this paper as Dehouche, 2021
GenAI poses fundamental challenges to the regulation of plagiarism and academic integrity.
“The fact that AI is trained with existing work and its ability to quickly generate content that may be unauthorized imitations impose fundamental challenges to the regulation of plagiarism and academic integrity [3,4].”
From AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities (2024) · cites this paper as [3]
The different stances on content-generating applications are due to concern centered on plagiarism because it is challenging to trace and verify the originality of AI-generated content.
“This is due to concern centered on plagiarism because it is challenging to trace and verify the originality of AI-generated content [59, 60].”
From Generative artificial intelligence (GAI) usage guidelines for scholarly publishing: a cross-sectional study of medical journals (2025) · cites this paper as [60]
In the education sector there is serious concern about academic integrity and plagiarism regarding GenAI.
“The public discourse surrounding GenAI has been mostly positive, but in the education sector there is serious concern about academic integrity and plagiarism (Dehouche, 2021;Lampropoulos et al., 2023;Sullivan et al., 2023;Yeo, 2023).”
From Not quite eye to A.I.: student and teacher perspectives on the use of generative artificial intelligence in the writing process (2023) · cites this paper as Dehouche, 2021
One last threat may be posed by human and artificial works being indistinguishable.
“Finally, one last threat may be posed by human and artificial works being indistinguishable (Dehouche, 2021).”
From On the creativity of large language models (2025) · cites this paper as Dehouche, 2021
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- Authors
- Dehouche, N.
- Affiliations
- Mahidol University (TH)
- Corresponding author
- Dehouche, N
- ORCID
- Dehouche, N
- Venue
- Ethics in Science and Environmental Politics
- Publisher
- Inter-Research Science Center
- Published
- 2021-03-25
- Volume
- 21
- Pages
- 17-23
- ISSN
- 1611-8014 (ISSN-L)
- 1863-5415
- OpenAlex
- W3125358881
- Cited by
- 296
- References
- 25
- Citations by year
- 2026: 24 · 2025: 67 · 2024: 87 · 2023: 98 · 2022: 12 · 2021: 8
- FWCI
- 64.07
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- public-domain
- creativecommons.org/licenses/by/4.0/ (vor)
- Locations
- Ethics in Science and Environmental Politics (publishedVersion, public-domain)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- PhilPapers (PhilPapers Foundation) (submittedVersion)
- Funders
- Mahidol University International College
- University of Oxford
- Mahidol University
- Région Normandie
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- plagiarism, GPT-3, generative pre-trained transformer, large language models, intellectual property, academic integrity, AI-generated text
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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