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

arXiv cs.CL / 4/13/2026

💬 OpinionSignals & Early TrendsIdeas & Deep AnalysisModels & Research

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Key Points

The paper (arXiv:2604.09501v1) stages a discussion among linguistics and computational language researchers about how modern language models can contribute to major questions in language science.
It argues against two common misconceptions: the “String Statistics Strawman” and the “As Good As it Gets Assumption,” which respectively underestimate LMs’ linguistic usefulness and overstate the limits of current LM research.
The authors clarify what kinds of scientific insights LM-based work can realistically provide about human language and about language models themselves.
They call for a more expansive, AI-age research agenda that addresses critics’ concerns to build a more robust science of both human language and LMs.

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.