Yevgeni Berzak

@whylikethis.bsky.social

Assistant Prof. at the Technion. Computational Psycholinguistics, NLP, Cognitive Science. https://lacclab.github.io/

Hallucinated references are the talk of the town now. How did we go about paper submissions with "hallucinated" references? At CoNLL, we viewed incorrect references as a violation of fundamental scholarly standards, and papers with such references were not sent for review.

my friend/colleague Frank Jäkel wrote a book on AI. I sadly don't know German but I happily know Frank, and I've heard him talking about this for a while now, and just on that basis I'd recommend the German speakers in the audience check it out

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If you haven't been looking recently at the Open Encyclopedia of Cognitive Science (oecs.mit.edu), here's your reminder that we are a free, open access resource for learning about the science of mind. Today we are launching our new Thematic Collections to organize our growing set of articles!

OECS thematic collections.

Made a new assignment for a class on Computational Psycholinguistics: - I trained a Transformer language model on sentences sampled from a PCFG - The students' task: Given the Transformer, try to infer the PCFG (w/ a leaderboard for who got closest) Would recommend! 1/n

On the left is a probabilistic context free grammar (PCFG). On the right is an image of the Transformer architecture. There are arrows going back and forth between the PCFG and the Transformer, showing how the assignment goes back and forth between them.

Check out our new work on introspection in LLMs! 🔍 TL;DR we find no evidence that LLMs have privileged access to their own knowledge. Beyond the study of LLM introspection, our findings inform an ongoing debate in linguistics research: prompting (eg grammaticality judgments) =/= prob measurement!

Siyuan Song@siyuansong.bsky.social · last yr.

New preprint w/ @jennhu.bsky.social @kmahowald.bsky.social : Can LLMs introspect about their knowledge of language? Across models and domains, we did not find evidence that LLMs have privileged access to their own predictions. 🧵(1/8)

Out today in Nature Machine Intelligence! From childhood on, people can create novel, playful, and creative goals. Models have yet to capture this ability. We propose a new way to represent goals and report a model that can generate human-like goals in a playful setting... 1/N

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