New NeurIPS paper! Why do LMs represent concepts linearly? We focus on LMs's tendency to linearly separate true and false assertions, and provide an analysis of the truth circuit in a toy model. A joint work with Gilad Yehudai, @tallinzen.bsky.social, Joan Bruna and @albertobietti.bsky.social.

1/8 Happy to share our new paper—“IQ Test for LLMs”—co-authored with Aviya Maimon, Amir DN Cohen, @neurogal.bsky.social and Reut Tsarfaty. We propose to rethink how language models are evaluated by focusing on the latent capabilities that explain benchmark results.
Arxiv: arxiv.org/pdf/2507.20208

I’ll be at #ACL2025! If you’re around and want to catch up or chat, please ping me!

How well can LLMs understand tasks with complex sets of instructions? We investigate through the lens of RELIC: REcognizing (formal) Languages In-Context, finding a significant overhang between what LLMs are able to do theoretically and how well they put this into practice.

Introduction \ updates post (better late than never):
- I recently graduated from ELSC @hebrewuniversity.bsky.social 🎉
- Moved to NYC 🗽 (view from our balcony below 👇)
- And started a postdoc in @columbiauniversity.bsky.social

Pls PM if you are in the NYC area and want to talk (or have a beer 🍻)

Our paper "A Practical Method for Generating String Counterfactuals" has been accepted to the findings of NAACL 2025! a joint work with @matan-avitan.bsky.social , @yoavgo.bsky.social and Ryan Cotterell. We propose "Intervention Lens", a technique to explain intervention in natural language. (1/6)

A quick update: I’ve completed my PhD at Bar-Ilan University. After an amazing research visit in Prof. Ryan Cotterell’s lab at ETH Zurich, I am super excited to join NYU Center for Data Science as a Faculty Fellow!

Happy to share our work "Counterfactual Generation from Language Models" with @AnejSvete, @vesteinns, and Ryan Cotterell! We tackle generating true counterfactual strings from LMs after interventions and introduce a simple algorithm for it. (1/7) arxiv.org/pdf/2411.07180