Ten simple rules to leverage large language models for getting grants
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Published guidance warns against hallucinated references, privacy leaks, overreliance, and funder-policy violations.
“Published guidance warns against hallucinated references, privacy leaks, overreliance, and funder-policy violations [8,19].”
From A Workflow for Grant Discovery and Proposal Development Using Large Language Models: Development and Formative Evaluation (2026) · cites this paper as [8]
These rules align with published recommendations that large language models can assist grant development but require fact-checking, privacy awareness, and researcher ownership of scientific and strategic content.
“These rules align with published recommendations that large language models can assist grant development but require fact-checking, privacy awareness, and researcher ownership of scientific and strategic content [8].”
From A Workflow for Grant Discovery and Proposal Development Using Large Language Models: Development and Formative Evaluation (2026) · cites this paper as [8]
This is consistent with published grant-writing guidance: large language models may support clarity, feedback, and iteration, but no model can replace funder-specific judgment, scientific ownership, or expert review.
“This is consistent with published grant-writing guidance: large language models may support clarity, feedback, and iteration, but no model can replace funder-specific judgment, scientific ownership, or expert review [8].”
From A Workflow for Grant Discovery and Proposal Development Using Large Language Models: Development and Formative Evaluation (2026) · cites this paper as [8]
Existing guidance on AI in grant writing focuses mainly on how individual researchers should use large language models responsibly.
“Existing guidance on AI in grant writing focuses mainly on how individual researchers should use large language models responsibly [8].”
From A Workflow for Grant Discovery and Proposal Development Using Large Language Models: Development and Formative Evaluation (2026) · cites this paper as [8]
Guidelines and practical strategies have been proposed to ensure ethical implementation and maintain scientific rigor.
“Guidelines and practical strategies have been proposed to ensure ethical implementation and maintain scientific rigor [8–10].”
From Integrating large language models in biostatistical workflows for clinical and translational research (2025) · cites this paper as [9]
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References 24 · Cited by 14
Research works printed but not linked: 18
- [2]Jannai D, Meron A, Lenz B, Levine Y, Shoham Y. Human or Not? A Gamified Approach to the Turing Test. ArXiv Prepr ArXiv230520010. 2023.
- [4]Huang J, Tan M. The role of ChatGPT in scientific communication: writing better scientific review articles. Am J Cancer Res. 2023; 13(4): 1148. 37168339 PMC10164801
- [6]Katsnelson A. Poor English skills? New AIs help researchers to write better. Nature. 2022 Aug 29; 609(7925): 208– 209. doi: 10.1038/d41586-022-02767-9 36038730 · DOI
- [7]Amano T, Ramírez-Castañeda V, Berdejo-Espinola V, Borokini I, Chowdhury S, Golivets M, et al. The manifold costs of being a non-native English speaker in science. PLoS Biol. 2023 Jul 18; 21(7): e3002184. doi: 10.1371/journal.pbio.3002184 37463136 PMC10353817 · DOI
- [8]Salvagno M, Taccone FS, Gerli AG. Can artificial intelligence help for scientific writing? Crit Care. 2023 Feb 25; 27(1): 75. doi: 10.1186/s13054-023-04380-2 36841840 PMC9960412 · DOI · Paper page
- [9]Tools such as ChatGPT threaten transparent science; here are our ground rules for their use. Nature. 2023 Jan 24; 613(7945): 612– 612. doi: 10.1038/d41586-023-00191-1 36694020 · DOI · Paper page
- [10]Gaggioli A. Ethics: disclose use of AI in scientific manuscripts. Nature. 2023 Feb 14; 614(7948): 413– 413. doi: 10.1038/d41586-023-00381-x 36788370 · DOI
- [11]Alkaissi H, McFarlane SI. Artificial hallucinations in ChatGPT: implications in scientific writing. 2023; 15(Cureus, 2). PMC9939079 36811129
- [12]Khurana D, Koli A, Khatter K, Singh S. Natural language processing: state of the art, current trends and challenges. Multimed Tools Appl. 2023; 82(3): 3713– 3744. doi: 10.1007/s11042-022-13428-4 35855771 PMC9281254 · DOI · Paper page
- [15]Quitadamo IJ, Kurtz MJ. Learning to improve: using writing to increase critical thinking performance in general education biology. CBE Life Sci Educ. 2007; 6(2): 140– 154. doi: 10.1187/cbe.06-11-0203 17548876 PMC1885902 · DOI
- [17]Change to policy on the use of generative AI and large language models [Internet]. [cited 2024 Jan 14]. Available from: https://www.science.org/content/blog-post/change-policy-use-generative-ai-and-large-language-models.
- [18]Springer Nature introduces Curie, its AI-powered scientific writing assistant. Springer Nature Group | Springer Nature [Internet]. [cited 2024 Jan 14]. Available from: https://group.springernature.com/gp/group/media/press-releases/ai-powered-scientific-writing-assitant-launched/26176230.
- [25]Emsley R. ChatGPT: these are not hallucinations–they’re fabrications and falsifications. Schizophrenia. 2023 Aug 19; 9(1): 1– 2. 37598184 PMC10439949
- [26]Athaluri SA, Manthena SV, Kesapragada VSRKM, Yarlagadda V, Dave T, Duddumpudi RTS. Exploring the Boundaries of Reality: Investigating the Phenomenon of Artificial Intelligence Hallucination in Scientific Writing Through ChatGPT References. Cureus. 2023 Apr; 15(4): e37432. doi: 10.7759/cureus.37432 37182055 PMC10173677 · DOI · Paper page
- [29]Straw I, Wu H. Investigating for bias in healthcare algorithms: a sex-stratified analysis of supervised machine learning models in liver disease prediction. BMJ Health Care Inform. 2022 Apr 24; 29(1): e100457. doi: 10.1136/bmjhci-2021-100457 35470133 PMC9039354 · DOI
- [31]Sohn E. Secrets to writing a winning grant. Nature. 2020; 577(7788): 133– 135. doi: 10.1038/d41586-019-03914-5 31863064 · DOI
- [32]Botham CM, Arribere JA, Brubaker SW, Beier KT. Ten simple rules for writing a career development award proposal. PLoS Comput Biol. 2017; 13(12): e1005863. doi: 10.1371/journal.pcbi.1005863 29240828 PMC5730102 · DOI
- [33]Yuan K, Cai L, Ngok SP, Ma L, Botham CM. Ten simple rules for writing a postdoctoral fellowship. PLoS Comput Biol. 2016; 12(7): e1004934. doi: 10.1371/journal.pcbi.1004934 27415752 PMC4945040 · DOI
- Authors
- Seckel, E., Stephens, B. Y., & Rodriguez, F.
- Affiliations
- Stanford Medicine (US)
- Corresponding author
- Seckel, Elizabeth
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2024-03-01
- Volume
- 20
- Issue
- 3
- Pages
- e1011863
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC10906892
- OpenAlex
- W4392361435
- Cited by
- 14
- References
- 24
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 2
- FWCI
- 5.22
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Heart, Lung, and Blood Institute (1K01HL144607)
- American Heart Association (20AMFDP35430002)
- Doris Duke Charitable Foundation (2022051)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Research, Science, and Academia
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Academic Writing and Publishing
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Keywords
- large language models, grant writing, research funding, AI-assisted writing
- MeSH terms
- Language
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