Large language models surpass human experts in predicting neuroscience results

Luo, Xiaoliang; Rechardt, Akilles; Sun, Guangzhi; Nejad, Kevin K.; Yáñez, Felipe; Yilmaz, Bati; Lee, Kangjoo; Cohen, Alexandra O.; Borghesani, Valentina; Pashkov, Anton; Marinazzo, Daniele; Nicholas, Jonathan; Salatiello, Alessandro; Sucholutsky, Ilia; Minervini, Pasquale; Razavi, Sepehr; Rocca, Roberta; Yusifov, Elkhan; Okalova, Tereza; Gu, Nianlong; Ferianc, Martin; Khona, Mikail; Patil, Kaustubh R.; Lee, Pui Shee; Mata, Rui; Myers, Nicholas E.; Bizley, Jennifer K.; Musslick, Sebastian; Bilgin, Isil Poyraz; Niso, Guiomar; Ales, Justin M.; Gaebler, Michael; Ratan Murty, N. Apurva; Loued-Khenissi, Leyla; Behler, Anna; Hall, Chloe M.; Dafflon, Jessica; Bao, Sherry Dongqi; Love, Bradley C.

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.

Citation

Luo, X., Rechardt, A., Sun, G., Nejad, K. K., Yáñez, F., Yilmaz, B., Lee, K., Cohen, A. O., Borghesani, V., Pashkov, A., Marinazzo, D., Nicholas, J., Salatiello, A., Sucholutsky, I., Minervini, P., Razavi, S., Rocca, R., Yusifov, E., Okalova, T., Gu, N., …Love, B. C. (2024). Large language models surpass human experts in predicting neuroscience results. Nature Human Behaviour, https://doi.org/10.1038/s41562-024-02046-9

• Journal Article Type: Article
• Acceptance Date: Oct 3, 2024
• Online Publication Date: Nov 27, 2024
• Publication Date: Jan 1, 2024
• Deposit Date: Dec 5, 2024
• Publicly Available Date: Dec 18, 2024
• Journal: Nature Human Behaviour
• Electronic ISSN: 2397-3374
• Publisher: Nature Publishing Group
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1038/s41562-024-02046-9
• Public URL: https://nottingham-repository.worktribe.com/output/42595003
• Publisher URL: https://www.nature.com/articles/s41562-024-02046-9