  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.
