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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)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Ai ; Large Language Models ; Science; Ai
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 0027-8424
e-ISSN 1091-6490
Zeitschrift Proceedings of the National Academy of Sciences of the United States of America
Quellenangaben Band: 122, Heft: 5, Seiten: , Artikelnummer: e2401227121 Supplement: ,
Verlag National Academy of Sciences
Verlagsort 2101 Constitution Ave Nw, Washington, Dc 20418 Usa
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-540011-001
G-530008-001
Förderungen 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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