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Binz, M. ; Alaniz, S. ; Roskies, A.* ; Aczel, B.* ; Bergstrom, C.T.* ; Allen, C.E.* ; Schad, D.* ; Wulff, D.U.* ; West, J.D.* ; Zhang, Q.* ; Shiffrin, R.M.* ; Gershman, S.J.* ; Popov, V.* ; Bender, E.M.* ; Marelli, M.* ; Botvinick, M.M.* ; Akata, Z. ; Schulz, E.

How should the advancement of large language models affect the practice of science?

Proc. Natl. Acad. Sci. U.S.A. 122:e2401227121 (2025)
Publ. Version/Full Text DOI PMC
Open Access Hybrid
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Large language models (LLMs) are being increasingly incorporated into scientific workflows. However, we have yet to fully grasp the implications of this integration. How should the advancement of large language models affect the practice of science? For this opinion piece, we have invited four diverse groups of scientists to reflect on this query, sharing their perspectives and engaging in debate. Schulz et al. make the argument that working with LLMs is not fundamentally different from working with human collaborators, while Bender et al. argue that LLMs are often misused and overhyped, and that their limitations warrant a focus on more specialized, easily interpretable tools. Marelli et al. emphasize the importance of transparent attribution and responsible use of LLMs. Finally, Botvinick and Gershman advocate that humans should retain responsibility for determining the scientific roadmap. To facilitate the discussion, the four perspectives are complemented with a response from each group. By putting these different perspectives in conversation, we aim to bring attention to important considerations within the academic community regarding the adoption of LLMs and their impact on both current and future scientific practices.
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Publication type Article: Journal article
Document type Review
Keywords Ai ; Large Language Models ; Science; Ai
Language english
Publication Year 2025
HGF-reported in Year 2025
ISSN (print) / ISBN 0027-8424
e-ISSN 1091-6490
Journal Proceedings of the National Academy of Sciences of the United States of America
Quellenangaben Volume: 122, Issue: 5, Pages: , Article Number: e2401227121 Supplement: ,
Publisher National Academy of Sciences
Publishing Place 2101 Constitution Ave Nw, Washington, Dc 20418 Usa
Reviewing status Peer reviewed
Institute(s) Institute of AI for Health (AIH)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-540011-001
G-530008-001
Grants Excellence Strategy of the German Federal and State Governments
Federal Ministry of Education and Research (Bundesministerium fur Bildung und Forschung) (Tubingen AI Center)
German Research Foundation (Deutsche Forschungsgemeinschaft)
European Research Council
DOI 10.1073/pnas.2401227121
WOS ID 001434798400004
Scopus ID 85216927526
PubMed ID 39869798
Erfassungsdatum 2025-03-25
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