Large language models surpass human experts in predicting neuroscience results
Abstrak
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. 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 were confident in their predictions, they were more likely to be correct, which presages a future where humans and LLMs team together to make discoveries. Our approach is not neuroscience-specific and is transferable to other knowledge-intensive endeavors.
Penulis (39)
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 Nicholas
Alessandro Salatiello
Ilia Sucholutsky
Pasquale Minervini
Sepehr Razavi
Roberta Rocca
Elkhan Yusifov
Tereza Okalova
Nianlong Gu
Martin Ferianc
Mikail Khona
Kaustubh R. Patil
Pui-Shee Lee
Rui Mata
Nicholas E. Myers
Jennifer K Bizley
Sebastian Musslick
Isil Poyraz Bilgin
Guiomar Niso
Justin M. Ales
Michael Gaebler
N Apurva Ratan Murty
Leyla Loued-Khenissi
Anna Behler
Chloe M. Hall
Jessica Dafflon
Sherry Dongqi Bao
Bradley C. Love
Akses Cepat
- Tahun Terbit
- 2024
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓