Roy Mayan

🧠 To reason over text and track entities, we find that language models use three types of 'pointers'! They were thought to rely only on a positional one—but when many entities appear, that system breaks down. Our new paper shows what these pointers are and how they interact 👇

Can we precisely erase conceptual knowledge from LLM parameters? Most methods are shallow, coarse, or overreach, adversely affecting related or general knowledge. We introduce🪝𝐏𝐈𝐒𝐂𝐄𝐒 — a general framework for Precise In-parameter Concept EraSure. 🧵 1/

How can we interpret LLM features at scale? 🤔 Current pipelines use activating inputs, which is costly and ignores how features causally affect model outputs! We propose efficient output-centric methods that better predict how steering a feature will affect model outputs. New preprint led by my student @GurYoav with dream team @Roym4498, Chen Agassy, and Atticus Geiger 🧵1/

These bracelets are awesome! Thank you Wiz 😎 @wiz_io #SecDevLove