🤖🧠NEW PAPER🧠🤖 Children & neural networks can learn syntax from linear strings of words. How do they do it? Our hypothesis: Word co-occurrence statistics provide cues to syntax! (I.e., a new type of bootstrapping to consider!) Paper: arxiv.org/abs/2605.20529 1/n
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Tom McCoy
Why they matter
Researcher with public evidence across AI research, NLP & language.
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Assistant professor at Yale Linguistics. Studying computational linguistics, cognitive science, and AI. He/him.
What they're sharing
[2605.20529] Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networks arxiv.org
[2605.10061] Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appear arxiv.org
The Emergent Symbolic Structure of Artificial Neural Networks arxiv.org
direct.mit.edu
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Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networks aclanthology.org
Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference aclanthology.org
Articles & links
🤖🧠 New commentary 🧠🤖 What role should large language models (LLMs) play in linguistics? I reflect on this question in a commentary now on arXiv: arxiv.org/abs/2605.10061 To appear in BBS as a commentary on @futrell.bsky.social and @kmahowald.bsky.social's excellent piece on LL…
Work done with @paulsoulos.bsky.social, @tallinzen.bsky.social, and Paul Smolensky For much more, see our paper! arxiv.org/abs/2608.29530 12/12
AI Weekly's analysis
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- A new preprint reports that neural networks' vector representations can be closely approximated by closed-form symbolic equations with behavior 'largely unchanged.'
- The substitution was tested on small networks trained to manipulate lists and on LLMs across arithmetic, logic, computer code, and language.
- The authors say the symbolic approximation also lets them steer an LLM's behavior via targeted interventions on its internal representations.
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Tom McCoy reposted
New paper out in @openmindjournal.bsky.social! "Large Language Models as Distributional Baselines for Language Tasks". With @jamichaelov.bsky.social , @camrobjones.bsky.social , Tyler Chang, and Ben Bergen. direct.mit.edu/opmi/article...
Link for "Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networks": aclanthology.org/2026.conll-m...
I’m excited about this paper because statistical regularities sometimes pose a problem for learning abstract syntax (e.g., aclanthology.org/P19-1334/), but these results give an example of how statistics can *support* the right abstractions! For more, see our paper: arxiv.org/…
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Recent commentary
🤖🧠NEW PAPER🧠🤖 (The result of an 8-year project!) LLMs seem very different from symbolic systems. Yet LLMs excel in symbolic domains (e.g., language/code/math). How do they do it? Our finding: LLM representations have implicit symbolic structure! Link in thread ⬇️ 1/n
Today at #acl2026nlp: Check out the panel on "Linguistics and NLP in the LLM Era", featuring Allyson Ettinger, @futrell.bsky.social, Zoey Liu, and me! 2:00 - 3:30 in Gaslamp C&D
In San Diego for #acl2026nlp! Excited to talk to people about connections between CogSci, Linguistics, and AI
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