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Co-Linguistics: AI-augmented Theory Construction in Linguistics

arXiv 2609.37635 ↗First posted 29 Sep 2026cs.CLcited by 0

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Abstract (arXiv)

LLMs have been studied in recent linguistics as potential models of humans' linguistic abilities. Here we discuss an entirely different use of AI, namely as a co-scientist, to help construct and assess linguistic theories (we refer to the result as "Co-Linguistics"). Since the 1960s, linguistics has developed theories that are in principle mathematically formalizable, often in the language of formal language theory or model theory. The AI revolution in mathematics will thus have consequences in linguistics-but with an essential twist: proving new theorems is rarely the linguist's goal. Rather, one seeks to find the best set of axioms to derive empirical statements. AI could accelerate research by making existing theories fully explicit, by comparing competing theories, and more ambitiously, by proposing new theories (in machine learning, this relates to "program induction"). It will also help assess theories by accelerating the identification and test of crucial predictions, thanks to unparalleled access to data (in machine learning, this relates to "active learning"). While the cycle from theory evaluation to theory construction may give rise to recursive and possibly autonomous improvement of linguistic theories, humans remain central: linguists provide scientific directions and evaluate theories conceptually, and experimental participants are needed to assess empirical predictions that are outside the reach of LLMs.

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References · 11

  1. 2024Mechanistic Interpretability for AI Safety -- A Reviewcited by 422
  2. 2019The emergence of number and syntax units in LSTM language modelscited by 94
  3. 2018What do RNN Language Models Learn about Filler-Gap Dependencies?cited by 86
  4. 2020Mechanisms for Handling Nested Dependencies in Neural-Network Language Models and Humanscited by 42
  5. 2023How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speechcited by 22
  6. 2023Large Linguistic Models: Investigating LLMs' metalinguistic abilitiescited by 18
  7. 2020CiwGAN and fiwGAN: Encoding information in acoustic data to model lexical learning with Generative Adversarial Networkscited by 15
  8. 2026EconCSLib: AI-Assisted Lean Formalization for Economics & Computation researchcited by 7
  9. 2024Grammaticality Representation in ChatGPT as Compared to Linguists and Laypeoplecited by 5
  10. 2025Newton to Einstein: Axiom-Based Discovery via Game Designcited by 1
  11. 2026Measuring Form and Function in Language Modelscited by 1

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