Exploring the role of large language models in the scientific method: from hypothesis to discovery
Exploring the role of large language models in the scientific method: from hypothesis to discovery
We review how Large Language Models (LLMs) are redefining the scientific method and explore their potential applications across different stages of the scientific cycle, from hypothesis testing to discovery. We conclude that, for LLMs to serve as relevant and effective creative engines and productivity enhancers, their deep integration into all steps of the scientific process should be pursued in collaboration and alignment with human scientific goals, with clear evaluation metrics.
Zhang, Yanbo
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Khan, Sumeer A.
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Mahmud, Adnan
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Yang, Huck
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Lavin, Alexander
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Levin, Michael
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Frey, Jeremy G.
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Dunnmon, Jared
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Evans, James
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Bundy, Alan
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Dzeroski, Saso
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Tegner, Jesper
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Zenil, Hector
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5 August 2025
Zhang, Yanbo
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Khan, Sumeer A.
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Mahmud, Adnan
a9a0bc76-a94a-464c-a15c-575e18a2d28f
Yang, Huck
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Lavin, Alexander
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Levin, Michael
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Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Dunnmon, Jared
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Evans, James
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Bundy, Alan
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Dzeroski, Saso
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Tegner, Jesper
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Zenil, Hector
5da6fe18-37e5-4b94-8c98-739a07023118
Zhang, Yanbo, Khan, Sumeer A., Mahmud, Adnan, Yang, Huck, Lavin, Alexander, Levin, Michael, Frey, Jeremy G., Dunnmon, Jared, Evans, James, Bundy, Alan, Dzeroski, Saso, Tegner, Jesper and Zenil, Hector
(2025)
Exploring the role of large language models in the scientific method: from hypothesis to discovery.
npj Artificial Intelligence, 1 (14), [14].
(doi:10.1038/s44387-025-00019-5).
Abstract
We review how Large Language Models (LLMs) are redefining the scientific method and explore their potential applications across different stages of the scientific cycle, from hypothesis testing to discovery. We conclude that, for LLMs to serve as relevant and effective creative engines and productivity enhancers, their deep integration into all steps of the scientific process should be pursued in collaboration and alignment with human scientific goals, with clear evaluation metrics.
Text
s44387-025-00019-5 (1)
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Accepted/In Press date: 23 June 2025
Published date: 5 August 2025
Identifiers
Local EPrints ID: 504087
URI: http://eprints.soton.ac.uk/id/eprint/504087
ISSN: 3005-1460
PURE UUID: 73e80065-943e-4c93-99c7-ac2d22817479
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Date deposited: 22 Aug 2025 17:00
Last modified: 23 Aug 2025 01:34
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Contributors
Author:
Yanbo Zhang
Author:
Sumeer A. Khan
Author:
Adnan Mahmud
Author:
Huck Yang
Author:
Alexander Lavin
Author:
Michael Levin
Author:
Jared Dunnmon
Author:
James Evans
Author:
Alan Bundy
Author:
Saso Dzeroski
Author:
Jesper Tegner
Author:
Hector Zenil
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