Language models as tools for investigating the distinction between possible and impossible natural languages
This work addresses the challenge of understanding human language acquisition for linguists and cognitive scientists, but it is incremental as it outlines a program rather than presenting new results.
The paper tackles the problem of distinguishing possible from impossible natural languages to uncover human language learning biases, proposing a research program using language models as investigative tools with iterative refinements to link to human cognition.
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.