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Language models as tools for investigating the distinction between possible and impossible natural languages

Published online by Cambridge University Press:  01 July 2026

Julie Kallini Open the ORCID record for Julie Kallini [Opens in a new window]  and
Christopher Potts Open the ORCID record for Christopher Potts [Opens in a new window]
Julie Kallini*
Affiliation:
Stanford University, Stanford, CA, USA kallini@stanford.edu cgpotts@stanford.edu
Christopher Potts*
Affiliation:
Stanford University, Stanford, CA, USA kallini@stanford.edu cgpotts@stanford.edu
*
*Corresponding author.
*Corresponding author.
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Abstract

We argue that language models (LMs) have strong potential as investigative tools for probing the distinction between possible and impossible natural languages and thus uncovering the inductive biases that support human language learning. We outline a phased research program in which LM architectures are iteratively refined to better discriminate between possible and impossible languages, supporting linking hypotheses to human cognition.

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Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 49 , 2026 , e211
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Copyright
© The Author(s), 2026. Published by Cambridge University Press

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Footnotes

†

Equal contribution. Author order determined alphabetically by last name.

References

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