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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.09394 |
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| _version_ | 1866909953748369408 |
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| author | Kallini, Julie Potts, Christopher |
| author_facet | Kallini, Julie Potts, Christopher |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_09394 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Language models as tools for investigating the distinction between possible and impossible natural languages Kallini, Julie Potts, Christopher Computation and Language 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. |
| title | Language models as tools for investigating the distinction between possible and impossible natural languages |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2512.09394 |