Can sparse autoencoders be used to decompose and interpret steering
  vectors?
Authors: name: Harry Mayne, name: Yushi Yang, name: Adam Mahdi

Steering vectors are a promising approach to control the behaviour of large
language models. However, their underlying mechanisms remain poorly understood.
While sparse autoencoders (SAEs) may offer a potential method to interpret
steering vectors, recent findings show that SAE-reconstructed vectors often
lack the steering properties of the original vectors. This paper investigates
why directly applying SAEs to steering vectors yields misleading
decompositions, identifying two reasons: (1) steering vectors fall outside the
input distribution for which SAEs are designed, and (2) steering vectors can
have meaningful negative projections in feature directions, which SAEs are not
designed to accommodate. These limitations hinder the direct use of SAEs for
interpreting steering vectors.