1 citations · 1 across the 8 of their papers we have counts for
3 papers · 1 filter
Embryology of a Language Model
George Wang, Garrett Baker, Andrew Gordon +1
Understanding how language models develop their internal computational structure is a central problem in the science of deep learning. While susceptibilities, drawn from statistica…
Structural Inference: Interpreting Small Language Models with Susceptibilities
Garrett Baker, George Wang, Jesse Hoogland +1
We develop a linear response framework for interpretability that treats a neural network as a Bayesian statistical mechanical system. A small perturbation of the data distribution,…
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…