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Large language models surpass human experts in predicting neuroscience results

Author

Listed:
  • Xiaoliang Luo

    (University College London)

  • Akilles Rechardt

    (University College London)

  • Guangzhi Sun

    (University of Cambridge)

  • Kevin K. Nejad

    (University of Oxford
    University of Bristol)

  • Felipe Yáñez

    (Max Planck Institute for Neurobiology of Behavior – caesar)

  • Bati Yilmaz

    (Bilkent University)

  • Kangjoo Lee

    (Yale University School of Medicine)

  • Alexandra O. Cohen

    (Emory University)

  • Valentina Borghesani

    (Université de Genève)

  • Anton Pashkov

    (Novosibirsk State Medical University
    FSBI
    Novosibirsk State Technical University)

  • Daniele Marinazzo

    (Ghent University)

  • Jonathan Nicholas

    (New York University)

  • Alessandro Salatiello

    (University of Tübingen)

  • Ilia Sucholutsky

    (Princeton University)

  • Pasquale Minervini

    (University of Edinburgh)

  • Sepehr Razavi

    (The University of Edinburgh)

  • Roberta Rocca

    (Aarhus University)

  • Elkhan Yusifov

    (University of Zurich)

  • Tereza Okalova

    (University of Pennsylvania)

  • Nianlong Gu

    (University of Zurich)

  • Martin Ferianc

    (University College London)

  • Mikail Khona

    (Massachusetts Institute of Technology)

  • Kaustubh R. Patil

    (Research Centre Jülich
    Heinrich Heine University Düsseldorf)

  • Pui-Shee Lee

    (Ludwig-Maximilians-University Munich
    Technical University of Munich)

  • Rui Mata

    (University of Basel)

  • Nicholas E. Myers

    (University of Nottingham)

  • Jennifer K. Bizley

    (University College London)

  • Sebastian Musslick

    (University of Osnabrück
    Brown University)

  • Isil Poyraz Bilgin

    (Centre de recherche de l’Institut universitaire de gériatrie de Montréal)

  • Guiomar Niso

    (CSIC)

  • Justin M. Ales

    (University of St Andrews)

  • Michael Gaebler

    (Max Planck Institute for Human Cognitive and Brain Sciences)

  • N. Apurva Ratan Murty

    (Georgia Institute of Technology)

  • Leyla Loued-Khenissi

    (Lausanne University Hospital)

  • Anna Behler

    (The University of Newcastle)

  • Chloe M. Hall

    (University Medical Center of the Johannes Gutenberg University
    Johannes Gutenberg University)

  • Jessica Dafflon

    (National Institute of Mental Health
    National Institute of Mental Health
    Valence Labs)

  • Sherry Dongqi Bao

    (University of Zurich)

  • Bradley C. Love

    (University College London
    The Alan Turing Institute)

Abstract

Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. Here, to evaluate this possibility, we created BrainBench, a forward-looking benchmark for predicting neuroscience results. We find that LLMs surpass experts in predicting experimental outcomes. BrainGPT, an LLM we tuned on the neuroscience literature, performed better yet. Like human experts, when LLMs indicated high confidence in their predictions, their responses were more likely to be correct, which presages a future where LLMs assist humans in making discoveries. Our approach is not neuroscience specific and is transferable to other knowledge-intensive endeavours.

Suggested Citation

  • Xiaoliang Luo & Akilles Rechardt & Guangzhi Sun & Kevin K. Nejad & Felipe Yáñez & Bati Yilmaz & Kangjoo Lee & Alexandra O. Cohen & Valentina Borghesani & Anton Pashkov & Daniele Marinazzo & Jonathan N, 2025. "Large language models surpass human experts in predicting neuroscience results," Nature Human Behaviour, Nature, vol. 9(2), pages 305-315, February.
  • Handle: RePEc:nat:nathum:v:9:y:2025:i:2:d:10.1038_s41562-024-02046-9
    DOI: 10.1038/s41562-024-02046-9
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    References listed on IDEAS

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