Decoding Dark Matter: Specialized Sparse Autoencoders for Interpreting
  Rare Concepts in Foundation Models
Authors: name: Aashiq Muhamed, name: Mona Diab, name: Virginia Smith

Understanding and mitigating the potential risks associated with foundation
models (FMs) hinges on developing effective interpretability methods. Sparse
Autoencoders (SAEs) have emerged as a promising tool for disentangling FM
representations, but they struggle to capture rare, yet crucial concepts in the
data. We introduce Specialized Sparse Autoencoders (SSAEs), designed to
illuminate these elusive dark matter features by focusing on specific
subdomains. We present a practical recipe for training SSAEs, demonstrating the
efficacy of dense retrieval for data selection and the benefits of Tilted
Empirical Risk Minimization as a training objective to improve concept recall.
Our evaluation of SSAEs on standard metrics, such as downstream perplexity and
L_0 sparsity, show that they effectively capture subdomain tail concepts,
exceeding the capabilities of general-purpose SAEs. We showcase the practical
utility of SSAEs in a case study on the Bias in Bios dataset, where SSAEs
achieve a 12.5\% increase in worst-group classification accuracy when applied
to remove spurious gender information. SSAEs provide a powerful new lens for
peering into the inner workings of FMs in subdomains.