Year: 2024
Type: preprint
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. 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.
Source: arXiv (Cornell University)
Authors: 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, Murty, N Apurva Ratan, Loued-Khenissi, Leyla, Behler, Anna, Hall, Chloe M., Dafflon, Jessica, Bao, Sherry Dongqi, Love, Bradley C.
Institutions: University College London, University of Cambridge, University of Bristol, University of Oxford, Bilkent University, Yale University, Emory University, University of Geneva, Ghent University, New York University, University of Tübingen, Princeton University, University of Edinburgh, Aarhus University, University of Zurich, University of Pennsylvania, Massachusetts Institute of Technology, Forschungszentrum Jülich, Heinrich Heine University Düsseldorf, Technical University of Munich, Ludwig-Maximilians-Universität München, University of Basel, University of Nottingham, Osnabrück University, Brown University, Institut Universitaire de Gériatrie de Montréal, Consejo Superior de Investigaciones Científicas, Instituto Cajal, University of St Andrews, Max Planck Institute for Human Cognitive and Brain Sciences, Centre Hospitalier Universitaire Vaudois, University of Newcastle Australia, Johannes Gutenberg University Mainz, University Medical Center of the Johannes Gutenberg University Mainz, National Institute of Mental Health
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4392575271