You Can't Fight in Here! This is BBS!

TL;DR

This paper discusses the role of modern language models in linguistic research, addressing misconceptions and advocating for a broader, more integrated scientific approach in the AI era.

Contribution

It clarifies common misconceptions about language models' linguistic competence and proposes an expanded research agenda for the language sciences in the context of AI.

Findings

Identifies the String Statistics Strawman as a misconception.

Critiques the As Good As it Gets assumption in LM research.

Advocates for a more expansive, interdisciplinary approach to studying language.

Abstract

Norm, the formal theoretical linguist, and Claudette, the computational language scientist, have a lovely time discussing whether modern language models can inform important questions in the language sciences. Just as they are about to part ways until they meet again, 25 of their closest friends show up -- from linguistics, neuroscience, cognitive science, psychology, philosophy, and computer science. We use this discussion to highlight what we see as some common underlying issues: the String Statistics Strawman (the mistaken idea that LMs can't be linguistically competent or interesting because they, like their Markov model predecessors, are statistical models that learn from strings) and the As Good As it Gets Assumption (the idea that LM research as it stands in 2026 is the limit of what it can tell us about linguistics). We clarify the role of LM-based work for scientific insights into human language and advocate for a more expansive research program for the language sciences in the AI age, one that takes on the commentators' concerns in order to produce a better and more robust science of both human language and of LMs.