Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appear
For theoretical linguists, it broadens the interpretability of language models as tools for testing diverse linguistic theories.
The paper argues that neural language models can instantiate formal, generative linguistic theories, not just gradient usage-based ones, expanding the space of testable theories and potentially reconciling the two traditions.
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.