Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appear

TL;DR

This paper argues that neural language models can support both gradient usage-based and formal generative linguistic theories, broadening their role in linguistic research.

Contribution

It demonstrates that language models are compatible with formal structural theories, not just gradient, usage-based approaches.

Findings

Language models can instantiate formal linguistic theories.

This expands the scope of theories testable with neural models.

Potential for reconciling different linguistic paradigms.

Abstract

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.