OrtSAE: Orthogonal Sparse Autoencoders Uncover Atomic Features
Authors: name: Anton Korznikov, name: Andrey Galichin, name: Alexey Dontsov, name: Oleg Rogov, name: Elena Tutubalina, name: Ivan Oseledets

Sparse autoencoders (SAEs) are a technique for sparse decomposition of neural
network activations into human-interpretable features. However, current SAEs
suffer from feature absorption, where specialized features capture instances of
general features creating representation holes, and feature composition, where
independent features merge into composite representations. In this work, we
introduce Orthogonal SAE (OrtSAE), a novel approach aimed to mitigate these
issues by enforcing orthogonality between the learned features. By implementing
a new training procedure that penalizes high pairwise cosine similarity between
SAE features, OrtSAE promotes the development of disentangled features while
scaling linearly with the SAE size, avoiding significant computational
overhead. We train OrtSAE across different models and layers and compare it
with other methods. We find that OrtSAE discovers 9% more distinct features,
reduces feature absorption (by 65%) and composition (by 15%), improves
performance on spurious correlation removal (+6%), and achieves on-par
performance for other downstream tasks compared to traditional SAEs.