You can’t fight in here! This is BBS!

2026/01/01 by Richard Futrell, Kyle Mahowald · 6 voices · 2 citations
Computer Science · Neuroscience · Social Sciences · #Expansive #Human language #Language and cultural evolution #Language model #Language technology #Limit (mathematics) #Natural Language Processing Techniques #Natural language #Neurobiology of Language and Bilingualism #On Language #Order (exchange) #String (physics)

paper · pdf · doi:10.1017/s0140525x26105123

published in Behavioral and Brain Sciences 49, e224 (Cambridge University Press)

openalex publication_date 2026/01/01 · openalex created_date 2026/04/15 · openalex updated_date 2026/08/01

Abstract

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.

Citations

• What enables human language? A biocultural framework
• Both Direct and Indirect Evidence Contribute to Dative Alternation Preferences in Language Models
• Deep Learning is Not So Mysterious or Different
• What’s Surprising About Surprisal
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• The metaphors of artificial intelligence
• Reclaiming AI as a Theoretical Tool for Cognitive Science
• Human-like Linguistic Biases in Neural Speech Models: Phonetic Categorization and Phonotactic Constraints in Wav2Vec2.0
• Decomposing modal thought.
• AI models collapse when trained on recursively generated data
• Filtered Corpus Training (FiCT) Shows that Language Models Can Generalize from Indirect Evidence
• The Platonic Representation Hypothesis
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• Power Hungry Processing: Watts Driving the Cost of AI Deployment?
• Over-reliance on English hinders cognitive science
• On Logical Inference over Brains, Behaviour, and Artificial Neural Networks
• Understanding deep learning (still) requires rethinking generalization
• Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex
• Expectation-based syntactic comprehension
• Critical period effects in second language learning: The influence of maturational state on the acquisition of English as a second language
• The TRACE model of speech perception

Cited by

• Perturbation: A simple and efficient adversarial tracer for representation learning in language models
• Differential syntactic and semantic encoding in LLMs

Discussions

• Richard @futrell.bsky.social and I have posted our response to the commentaries on our BBS target article "How Linguistics Learned to Stop Worrying and Love the Language Models." The response is: "You [bsky, 35 points, 1 comments]
• And the award for best paper title and abstract in 2026 so far goes to @futrell.bsky.social & @kmahowald.bsky.social and their response to commentaries in Behavioral and Brain Sciences arxiv.org/pdf/2 [bsky, 8 points, 0 comments]
• Open request to folks in-the-know on linguistics. Mahowald and Futrell wrote a BBS article on LLMs in linguistics last, year, and followed up with this response article to commentators: arxiv.org/pdf/ [bsky, 4 points, 1 comments]
• I personally feel this is a particularly exciting program in light of all the discussion to my colleagues' (@futrell.bsky.social and @kmahowald.bsky.social ) new response (with a wonderfully clever ti [bsky, 1 points, 0 comments]
• Source: arxiv.org/pdf/2604.09501 [bsky, 1 points, 0 comments]
• 📝 Futrell & Mahowald in BBS — @symbolicstorage Best paper title of 2026? Points to a response by @rljfutrell & @kmahowald in Behavioral and Brain Sciences on language modeling and cognitive plausibil [bsky, 0 points, 1 comments]

Related

• Kallini et al. (2024) Do Not Compare Impossible Languages with Constituency-based Ones
• The Quo Vadis of the Relationship between Language and Large Language Models
• Large language models have learned to use language
• Stochastic LLMs do not Understand Language: Towards Symbolic, Explainable and Ontologically Based LLMs
• How linguistics learned to stop worrying and love the language models