Now published in open access! Your one-stop shop for the philosophy of language models. It's the spiritual descendant of our two-part preprint from 2024, fully updated. This should be particularly useful for anyone looking for an entry point into this rapidly growing field.
The success of large language models (LLMs) across many domains of AI research has generated intense debate. Some attribute their impressive performance on complex tasks to human-like linguistic and ...
Looking forward to speaking at this ICML workshop on ML & Philosophy! Check out the full lineup and CFP below (deadline: May 11th). Despite the title, the CFP is open to work in many areas of the philosophy of AI, not just AI ethics.
sites.google.com/view/philmli...
Looking foward to this!
The LLM has to do something like schema-conditioned infilling: produce a high-probability member of the equivalence class consistent with those constraints. So I'm not sure how unexpected the results are? That's roughly what I'd expect from matching to structurally similar training passages. 2/3
The main takeaway for me is that structural information in language is far more constraining than intuition suggests. That's very interesting (and I agree that parrot metaphors are misleading) but it seems like a claim about language more than intelligence. 3/3
Very cool idea! Some quick thoughts. It looks like the corruption preserves a lot of information (function words, morphology, word order, punctuation, numbers, register) which would strongly constrains the posterior over plausible discourse frames as it were. 1/3
Raphaël Millière
now accepted at ICLR! 🐺🥳🐺
arxiv.org/abs/2506.20666
With @jesusoxford.bsky.social we are looking for a Professor of Statistics.
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About the role: tinyurl.com/b8uy6mr5
Deadline: 15 September
