🤖🧠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
Who's Who of AI
Tom McCoy
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
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…
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/…
Their network
In Tom McCoy's orbit
Center = Tom McCoy. Left = members they follow (green edges). Right = members who follow them (blue edges). Top = mutual follows (orange edges, slightly larger). Drag any node to reposition; click to open that profile.