title: Can sparse autoencoders be used to decompose and interpret steering
    vectors?
summary: 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.
paper_authors._id: 6735c8b06d4b6c741016a221
paper_authors.name: Harry Mayne
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-15T14:19:33.411Z
paper_authors._id: 63f887bcdf053017d1ad2967
paper_authors.avatarUrl: /avatars/8dd4d160456348a92d4269b73ff4e77e.svg
paper_authors.fullname: Harry Mayne
paper_authors.type: user
paper_authors.user: HarryMayne
paper_authors._id: 6735c8b06d4b6c741016a222
paper_authors.name: Yushi Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-15T14:23:16.505Z
paper_authors._id: 6562737bf5532ac1bd09d3bd
paper_authors.avatarUrl: /avatars/aaaff49b6abb5f9351159006b7755d25.svg
paper_authors.fullname: YY
paper_authors.type: user
paper_authors.user: yy0514
paper_authors._id: 6735c8b06d4b6c741016a223
paper_authors.name: Adam Mahdi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-20T10:09:14.308Z
paper_authors._id: 673cf9c93af47d1d2b6584c9
paper_authors.avatarUrl: /avatars/0792921a8adaa1493255abb0b5aaa4f1.svg
paper_authors.fullname: Adam Mahdi
paper_authors.type: user
paper_authors.user: ammaox
paper_title: Can sparse autoencoders be used to decompose and interpret steering
    vectors?
paper_summary: 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.
paper_ai_keywords: steering vectors sparse autoencoders (SAEs) input distribution feature directions