9 citations · 9 across the 4 of their papers we have counts for
4 papers · 1 filter
You Are What You Eat -- AI Alignment Requires Understanding How Data Shapes Structure and Generalisation
Simon Pepin Lehalleur, Jesse Hoogland, Matthew Farrugia-Roberts +5
In this position paper, we argue that understanding the relation between structure in the data distribution and structure in trained models is central to AI alignment. First, we di…
Dynamics of Transient Structure in In-Context Linear Regression Transformers
Liam Carroll, Jesse Hoogland, Matthew Farrugia-Roberts +1
Modern deep neural networks display striking examples of rich internal computational structure. Uncovering principles governing the development of such structure is a priority for…
Open Problems in Mechanistic Interpretability
Lee Sharkey, Bilal Chughtai, Joshua Batson +26
Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities in order to accomplish concrete scientific and engineering goa…
Differentiation and Specialization of Attention Heads via the Refined Local Learning Coefficient
George Wang, Jesse Hoogland, Stan van Wingerden +2
We introduce refined variants of the Local Learning Coefficient (LLC), a measure of model complexity grounded in singular learning theory, to study the development of internal stru…