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| Hauptseite > Publikationsdatenbank > Large language models surpass human experts in predicting neuroscience results > print |
| Hauptseite > Publikationsdatenbank > Large language models surpass human experts in predicting neuroscience results > print |
| 001 | 1041321 | ||
| 005 | 20250512115732.0 | ||
| 024 | 7 | _ | |a 10.1038/s41562-024-02046-9 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2025-02220 |2 datacite_doi |
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| 037 | _ | _ | |a FZJ-2025-02220 |
| 082 | _ | _ | |a 150 |
| 100 | 1 | _ | |a Luo, Xiaoliang |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Large language models surpass human experts in predicting neuroscience results |
| 260 | _ | _ | |a London |c 2025 |b Nature Research |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1744792024_17200 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a cientific 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. |
| 536 | _ | _ | |a 5254 - Neuroscientific Data Analytics and AI (POF4-525) |0 G:(DE-HGF)POF4-5254 |c POF4-525 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Rechardt, Akilles |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Sun, Guangzhi |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Nejad, Kevin K. |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Yáñez, Felipe |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Yilmaz, Bati |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Lee, Kangjoo |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Cohen, Alexandra O. |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Borghesani, Valentina |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Pashkov, Anton |0 P:(DE-HGF)0 |b 9 |
| 700 | 1 | _ | |a Marinazzo, Daniele |0 P:(DE-HGF)0 |b 10 |
| 700 | 1 | _ | |a Nicholas, Jonathan |0 P:(DE-HGF)0 |b 11 |
| 700 | 1 | _ | |a Salatiello, Alessandro |0 P:(DE-HGF)0 |b 12 |
| 700 | 1 | _ | |a Sucholutsky, Ilia |0 P:(DE-HGF)0 |b 13 |
| 700 | 1 | _ | |a Minervini, Pasquale |0 P:(DE-HGF)0 |b 14 |
| 700 | 1 | _ | |a Razavi, Sepehr |0 P:(DE-HGF)0 |b 15 |
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| 700 | 1 | _ | |a Mata, Rui |0 P:(DE-HGF)0 |b 24 |
| 700 | 1 | _ | |a Myers, Nicholas E. |0 P:(DE-HGF)0 |b 25 |
| 700 | 1 | _ | |a Bizley, Jennifer K. |0 P:(DE-HGF)0 |b 26 |
| 700 | 1 | _ | |a Musslick, Sebastian |0 P:(DE-HGF)0 |b 27 |
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| 700 | 1 | _ | |a Loued-Khenissi, Leyla |0 P:(DE-HGF)0 |b 33 |
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| 700 | 1 | _ | |a Hall, Chloe M. |0 P:(DE-HGF)0 |b 35 |
| 700 | 1 | _ | |a Dafflon, Jessica |0 P:(DE-HGF)0 |b 36 |
| 700 | 1 | _ | |a Bao, Sherry Dongqi |0 P:(DE-HGF)0 |b 37 |
| 700 | 1 | _ | |a Love, Bradley C. |0 P:(DE-HGF)0 |b 38 |
| 773 | _ | _ | |a 10.1038/s41562-024-02046-9 |g Vol. 9, no. 2, p. 305 - 315 |0 PERI:(DE-600)2885046-4 |n 2 |p 305 - 315 |t Nature human behaviour |v 9 |y 2025 |x 2397-3374 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1041321/files/s41562-024-02046-9.pdf |y OpenAccess |
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