---
title: "Large language models surpass human experts in predicting"
description: "The authors instead built BrainBench, a forward-looking test based on recent Journal of Neuroscience… 34 references, cited by 20 papers here."
authors: ["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"]
year: 2024
venue: "arXiv:2403.03230"
source: "https://arxiv.org/abs/2403.03230"
doi: "10.1038/s41562-024-02046-9"
arxiv: "2403.03230"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/luo-2024-large-language-models-surpass/"
---
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# Large language models surpass human experts in predicting neuroscience results

**Authors:** Xiaoliang Luo, Akilles Rechardt, [Guangzhi Sun](https://md.research.elamur.ai/free/en/authors/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](https://md.research.elamur.ai/free/en/authors/ilia-sucholutsky/), [Pasquale Minervini](https://md.research.elamur.ai/free/en/authors/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 and Bradley C. Love · **Year:** 2024 · **Venue:** arXiv:2403.03230 · **Publisher:** [Nature Portfolio](https://md.research.elamur.ai/free/en/publishers/nature-portfolio/) · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://doi.org/10.1038/s41562-024-02046-9) · [PDF](https://arxiv.org/pdf/2403.03230) · [DOI 10.1038/s41562-024-02046-9](https://doi.org/10.1038/s41562-024-02046-9) · [arXiv 2403.03230](https://arxiv.org/abs/2403.03230) · [OpenAlex](https://openalex.org/W4404764563)

**Citations:** 139 citations (OpenAlex, 2026-10-14) · [20](https://md.research.elamur.ai/free/en/papers/luo-2024-large-language-models-surpass/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper asks whether large language models can forecast the outcomes of neuroscience experiments, a task that may exceed what human experts can do as the literature keeps growing. Existing science benchmarks such as MMLU, PubMedQA and MedMCQA mostly test recall of established facts. The authors instead built BrainBench, a forward-looking test based on recent Journal of Neuroscience abstracts. Each item pairs the original abstract with a version whose results were rewritten to say something different but still read coherently. Both human experts and LLMs must identify the real one.

Models choose the option with lower perplexity, and their confidence is measured by the perplexity gap between the two versions. LLMs outperformed human experts. BrainGPT, a LLaMa2-7B model adapted to neuroscience abstracts with LoRA, did better still. For both models and humans, higher confidence went with higher accuracy, which suggests humans and LLMs could work well as teams. The approach could transfer to other knowledge-heavy fields.

## Key points
- BrainBench spans five subfields: Behavioral/Cognitive, Systems/Circuits, Neurobiology of Disease, Cellular/Molecular and Developmental/Plasticity/Repair. Behavioral/Cognitive items are the most frequent, at about 38% of the GPT-4-generated cases.
- Fifteen LLMs were evaluated, including the Galactica, Falcon, Llama-2 and Mistral families. They beat human experts in every subfield, and base models did better than their chat and instruct variants.
- LLMs largely agreed with one another about which items were hard (mean Spearman 0.83 ± 0.06). Their agreement with human experts was much weaker (about 0.27 ± 0.05).
- BrainGPT was built with LoRA, a parameter-efficient method that freezes the pretrained LLaMa2 weights and trains only small low-rank matrices.
- Confidence was well calibrated for both humans and all LLMs.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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Also on the byline: [Instituto Cajal](https://ror.org/012gwbh42) · [Novosibirsk State Medical University](https://ror.org/00d167n54) · [Centre Hospitalier Universitaire Vaudois](https://ror.org/05a353079) · [Institut Universitaire de Gériatrie de Montréal](https://ror.org/031z68d90) · [Max Planck Institute for Human Cognitive and Brain Sciences](https://ror.org/0387jng26) · [Federal Neurosurgical Center Novosibirsk](https://ror.org/04tmsfn31) · [Max Planck Institute for Neurobiology of Behavior – caesar](https://ror.org/02yjyfs84).

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## Cite

Cite as: 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 and Bradley C. Love. 2024. Large language models surpass human experts in predicting neuroscience results. arXiv:2403.03230.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{luo2024large,
  title = {{Large language models surpass human experts in predicting neuroscience results}},
  author = {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 and Bradley C. Love},
  year = {2024},
  doi = {10.1038/s41562-024-02046-9},
  eprint = {2403.03230},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1038/s41562-024-02046-9}
}
```

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*Updated 3 November 2026*