Differentiation and Specialization of Attention Heads via the Refined Local Learning Coefficient
George Wang Jesse Hoogland Stan van Wingerden Zach Furman Daniel Murfet
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 structure in transformer language models during training. By applying these refined LLCs (rLLCs) to individual components of a two-layer attention-only transformer, we gain novel insights into the progressive differentiation and specialization of attention heads. Our methodology reveals how attention heads differentiate into distinct functional roles over the course of training, analyzes the types of data these heads specialize to process, and discovers a previously unidentified multigram circuit. These findings demonstrate that rLLCs provide a principled, quantitative toolkit for developmental interpretability, which aims to understand models through their evolution across the learning process. This work advances the field of developmental interpretability by providing a mathematically rigorous approach to understanding neural networks through the lens of their learning process. More broadly, this work takes a step towards establishing the correspondence between data distributional structure, geometric properties of the loss landscape, learning dynamics, and emergent computational structures in neural networks.
Developmental Interpretability, Mechanistic Interpretability, Singular Learning Theory, Learning Dynamics, Stagewise development, Model complexity
The paper studies how language model attention heads develop during training using new, refined variants of the Local Learning Coefficient (LLC)