Hostname: page-component-76d6cb85b7-kcxw8 Total loading time: 0 Render date: 2026-07-11T11:54:22.407Z Has data issue: false hasContentIssue false
  • English
  • Français

Large language models are not about natural language

Published online by Cambridge University Press:  01 July 2026

Johan J. Bolhuis ,
Andrea Moro Open the ORCID record for Andrea Moro [Opens in a new window] ,
Stephen Crain  and
Sandiway Fong Open the ORCID record for Sandiway Fong [Opens in a new window]
Johan J. Bolhuis*
Affiliation:
Department of Psychology, University of Cambridge, Cambridge, UK j.j.bolhuis@uu.nl
Andrea Moro
Affiliation:
University School for Advanced Studies, Pavia & Scuola Normale Superiore, Pisa, Italy andrea.moro@iusspavia.it
Stephen Crain
Affiliation:
Department of Linguistics, Macquarie University, Sydney, Australia stephen.crain@mq.edu.au
Sandiway Fong
Affiliation:
Department of Linguistics, University of Arizona, Tucson, Arizona, USA sandiway@arizona.edu
*
*Corresponding author.
Get access Rights & Permissions [Opens in a new window]

Abstract

Large Language Models are useless for linguistics, as they are probabilistic models that require a vast amount of data to analyze externalized strings of words. In contrast, human language is underpinned by a mind-internal computational system that recursively generates hierarchical thought structures. The language system grows with minimal external input and can readily distinguish between real language and impossible languages.

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 49 , 2026 , e201
Check for updates
Copyright
© The Author(s), 2026. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Bolhuis, J. J., Crain, S., & Roberts, I. (2023). Language and learning: The cognitive revolution at 60-odd. Biological Reviews, 98, 931–941. https://doi.org/10.1111/brv.12936 CrossRefGoogle ScholarPubMed
Bolhuis, J. J., Crain, S., Fong, S., & Moro, A. (2024). AI doesn’t model human language. Nature, 627, 489. https://doi.org/10.1038/d41586-024-00824-z Google ScholarPubMed
Bowers, J. S. (2025). The successes and failures of artificial neural networks (ANNs) highlight the importance of innate linguistic priors for human language acquisition. Psychological Review, in press. Advance online publication. https://doi.org/10.1037/rev0000595 Google ScholarPubMed
Chomsky, N. (1971). Problems of knowledge and freedom: The Russell lectures. Pantheon Books.Google Scholar
Chomsky, N. (2000). Minimalist inquiries: The framework. In Martin, R., Michaels, D., & Uriagereka, J. (Eds.), Step by step: Essays on minimalist syntax in honor of Howard Lasnik (pp. 89–155). MIT Press.Google Scholar
Crain, S. (2012). The emergence of meaning. Cambridge University Press.10.1017/CBO9780511842863CrossRefGoogle Scholar
Crain, S., Koring, L., & Thornton, R. (2017). Language acquisition from a biolinguistic perspective. Neuroscience & Biobehavioral Reviews, 81, 120–149. https://doi.org/10.1016/j.neubiorev.2016.09.004 Google ScholarPubMed
Everaert, M. B., Huybregts, M. A., Chomsky, N., Berwick, R. C., & Bolhuis, J. J. (2015). Structures, not strings: Linguistics as part of the cognitive sciences. Trends in Cognitive Sciences, 19, 729–743. https://doi.org/10.1016/j.tics.2015.09.008 CrossRefGoogle Scholar
Fong, S. (2025). The generative enterprise is alive. Italian Journal of Linguistics, 37, 83–106. https://doi.org/10.26346/1120-2726-239 Google Scholar
Friederici, A. D., Chomsky, N., Berwick, R. C., Moro, A. & Bolhuis, J. J. (2017). Language, mind and brain. Nature Human Behaviour, 1, 713–722. https://doi.org/10.1038/s41562-017-0184-4 CrossRefGoogle ScholarPubMed
Kallini, J., Papadimitriou, I., Futrell, R., Mahowald, K., & Potts, C. (2024). Mission: Impossible language models. In Ku, L.-W., Martins, A., & Srikumar, V. (Eds.), Proceedings of the 62nd annual meeting of the association for computational linguistics (Volume 1: Long Papers) (pp. 14691–14714). Association for Computational Linguistics. https://doi.org/10.48550/arXiv.2401.06416 Google Scholar
Kerr, D. (2024). How Memphis became a battleground over Elon Musk’s xAI supercomputer. NPR 11 September 2024. Retrieved from www.npr.org/2024/09/11/nx-s1-5088134/elon-musk-ai-xai-supercomputermemphis-pollution Google Scholar
Lawson, A. (2024). Google to buy nuclear power for AI datacentres in‘world first’ deal. Guardian 15 October 2024. Retrieved from www.theguardian.com/technology/2024/oct/15/google-buy-nuclear-power-ai-datacentreskairos-power Google Scholar
Ling, J. (2001). Power of a human brain. In Physics factbook. Retrieved from hypertextbook.com/facts/2001/JacquelineLing.shtml Google Scholar
Luo, X, Ramscar, M., & Love, B. C. (2024). Beyond human-like processing: Large language models perform equivalently on forward and backward scientific text. arXiv:2411.11061v1 [cs.CL]. https://doi.org/10.48550/arXiv.2411.11061 CrossRefGoogle Scholar
Markov, A. A. (1913/2006). An example of statistical investigation of the text Eugene Onegin concerning the connection of samples in chains. Science in Context, 19(4), 591–600. https://doi.org/10.1017/S0269889706001074 CrossRefGoogle Scholar
Mitchell, J. & Bowers, J. (2020). Priorless recurrent networks learn curiously. In Proceedings of the 28th International Conference on Computational Linguistics (pp. 5147–5158). https://doi.org/10.18653/v1/2020.coling-main.451 CrossRefGoogle Scholar
Moro, A. (2016). Impossible languages. MIT Press.10.7551/mitpress/9780262034890.001.0001CrossRefGoogle Scholar
Moro, A., Greco, M. & Cappa, S. F. (2023). Large languages, impossible languages and human brains. Cortex, 167, 82–85. https://doi.org/10.1016/j.cortex.2023.07.003 CrossRefGoogle ScholarPubMed
Musso, M., Moro, A., Glauche, V., Rijntjes, M., Reichenbach, J., Büchel, C. & Weiller, C. (2003). Broca’s area and the language instinct. Nature Neuroscience, 6, 774–781. https://doi.org/10.1038/nn1077 CrossRefGoogle ScholarPubMed
Rizzi, L. (2009). The discovery of language invariance and variation, and its relevance for the cognitive sciences. Behavioral and Brain Sciences, 32, 467–468. https://doi.org/10.1017/S0140525X09990574 CrossRefGoogle Scholar
Tettamanti, M., Alkadhi, H., Moro, A., Perani, D., Kollias, S., & Weniger, D. (2002). Neural correlates for the acquisition of natural language syntax, NeuroImage, 17, 700–709. https://doi.org/10.1006/nimg.2002.1201 CrossRefGoogle ScholarPubMed
Yang, C., Crain, S., Berwick, R. C., Chomsky, N. & Bolhuis, J. J. (2017). The growth of language: Universal grammar, experience, and principles of computation. Neuroscience & Biobehavioral Reviews, 81, 103–119. https://doi.org/10.1016/j.neubiorev.2016.12.023 CrossRefGoogle ScholarPubMed
Ziv, I., Chemmla, E., Lan, N., & Katzir, R. (2025). Biasless language models learn unnaturally: How LLM fail to distinguisch the possible from the impossible. arXiv:2510.07178v1 [cs.CL]. https://arxiv.org/abs/2510.07178v1 https://doi.org/10.48550/arXiv.2510.07178 Google Scholar