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LLM use in scholarly writing poses a provenance problem

Nature Machine Intelligence volume 7, pages 1889–1890 (2025)Cite this article

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Suppose the LLM trained on, but does not mention, Smith’s text. And suppose we have never read it or even heard of Smith or her work. So, we don’t cite the paper either. Still, our essay now inherits — via nebulous, machine-mediated means — a distinctive insight that Smith developed but for which she receives no credit.

This scenario exemplifies what we call the ‘provenance problem’: a systematic breakdown in the chain of scholarly acknowledgement that current ethical frameworks fail to address. Unlike conventional plagiarism, this phenomenon involves neither ill-intent nor accidental carelessness with one’s sources. The problem is of a different, more intractable sort. Because LLM training distributes the influence of its source across billions of interdependent parameters, the contribution of any specific text is often untraceable.

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Fig. 1: Using an LLM to draft a scholarly article.

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Acknowledgements

During the drafting of this paper, GPT-5 and Claude Sonnet 4.5 were used to help edit and shorten a longer draft written by the authors. The authors then further edited and refined the text by hand. Such use and this acknowledgement adhere to proposed ethical guidelines for generative AI use and acknowledgement in academic research6. Each author has made a substantial contribution to the work, which has been thoroughly vetted for accuracy, and assumes responsibility for the integrity of their contributions5.

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Authors and Affiliations

  1. Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Queenstown, Singapore

    Brian D. Earp & Sebastian Porsdam Mann

  2. Georgetown Preparatory School, North Bethesda, MD, USA

    Haotian Yuan

  3. Monash Bioethics Centre, Monash University, Melbourne, Victoria, Australia

    Julian Koplin

  4. Centre for Advanced Studies in Bioscience Innovation Law (CeBIL), Faculty of Law, University of Copenhagen, Copenhagen, Denmark

    Sebastian Porsdam Mann

Authors
  1. Brian D. Earp
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  2. Haotian Yuan
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  3. Julian Koplin
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  4. Sebastian Porsdam Mann
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Correspondence to Brian D. Earp.

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The authors declare no competing interests.

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Earp, B.D., Yuan, H., Koplin, J. et al. LLM use in scholarly writing poses a provenance problem. Nat Mach Intell 7, 1889–1890 (2025). https://doi.org/10.1038/s42256-025-01159-8

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