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

Julie Kallini, Christopher Potts
12/10/2025
cs.CL

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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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Cite this paper

@article{kallini2025language,
  title  = {Language models as tools for investigating the distinction between possible and impossible natural languages},
  author = {Julie Kallini and Christopher Potts},
  year   = {2025},
  eprint = {2512.09394},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2512.09394v1}
}

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