Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appear
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914552037244928 |
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| author | McCoy, R. Thomas |
| author_facet | McCoy, R. Thomas |
| contents | Futrell and Mahowald (2025) frame the success of neural language models (LMs) as supporting gradient, usage-based linguistic theories. I argue that LMs can also instantiate theories based on formal structures - the types of theories seen in the generative tradition. This argument expands the space of theories that can be tested with LMs, potentially enabling reconciliations between usage-based and generative accounts. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_10061 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appear McCoy, R. Thomas Computation and Language Artificial Intelligence Futrell and Mahowald (2025) frame the success of neural language models (LMs) as supporting gradient, usage-based linguistic theories. I argue that LMs can also instantiate theories based on formal structures - the types of theories seen in the generative tradition. This argument expands the space of theories that can be tested with LMs, potentially enabling reconciliations between usage-based and generative accounts. |
| title | Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appear |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2605.10061 |