title: Measuring Progress in Dictionary Learning for Language Model
    Interpretability with Board Game Models
summary: What latent features are encoded in language model (LM) representations?
  Recent work on training sparse autoencoders (SAEs) to disentangle interpretable
  features in LM representations has shown significant promise. However,
  evaluating the quality of these SAEs is difficult because we lack a
  ground-truth collection of interpretable features that we expect good SAEs to
  recover. We thus propose to measure progress in interpretable dictionary
  learning by working in the setting of LMs trained on chess and Othello
  transcripts. These settings carry natural collections of interpretable features
  -- for example, "there is a knight on F3" -- which we leverage into
  supervised metrics for SAE quality. To guide progress in
  interpretable dictionary learning, we introduce a new SAE training technique,
  p-annealing, which improves performance on prior unsupervised
  metrics as well as our new metrics.
paper_authors._id: 66b0838e2782988785c3f445
paper_authors.name: Adam Karvonen
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-08-05T10:41:31.879Z
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paper_authors.avatarUrl: /avatars/cb63703500ab6878b6fd9c3a6c465527.svg
paper_authors.fullname: Adam Karvonen
paper_authors.type: user
paper_authors.user: adamkarvonen
paper_authors._id: 66b0838e2782988785c3f446
paper_authors.name: Benjamin Wright
paper_authors._id: 66b0838e2782988785c3f447
paper_authors.name: Can Rager
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-05T13:20:58.313Z
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paper_authors.avatarUrl: /avatars/d47dd0e4fc5bd08cf4f0c2c1355ea004.svg
paper_authors.fullname: Can Rager
paper_authors.type: user
paper_authors.user: canrager
paper_authors._id: 66b0838e2782988785c3f448
paper_authors.name: Rico Angell
paper_authors._id: 66b0838e2782988785c3f449
paper_authors.name: Jannik Brinkmann
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-05T13:21:10.461Z
paper_authors._id: 62fe3051029f73cec2766f6f
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paper_authors.fullname: Jannik Brinkmann
paper_authors.type: user
paper_authors.user: jbrinkma
paper_authors._id: 66b0838e2782988785c3f44a
paper_authors.name: Logan Smith
paper_authors._id: 66b0838e2782988785c3f44b
paper_authors.name: Claudio Mayrink Verdun
paper_authors._id: 66b0838e2782988785c3f44c
paper_authors.name: David Bau
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-05T13:22:00.956Z
paper_authors._id: 6214d6c01e35c843d42d1f77
paper_authors.avatarUrl: /avatars/ac208cd180b4f3ed1ec367e581facfcf.svg
paper_authors.fullname: David Bau
paper_authors.type: user
paper_authors.user: davidbau
paper_authors._id: 66b0838e2782988785c3f44d
paper_authors.name: Samuel Marks
paper_title: Measuring Progress in Dictionary Learning for Language Model
    Interpretability with Board Game Models
paper_summary: What latent features are encoded in language model (LM) representations?
  Recent work on training sparse autoencoders (SAEs) to disentangle interpretable
  features in LM representations has shown significant promise. However,
  evaluating the quality of these SAEs is difficult because we lack a
  ground-truth collection of interpretable features that we expect good SAEs to
  recover. We thus propose to measure progress in interpretable dictionary
  learning by working in the setting of LMs trained on chess and Othello
  transcripts. These settings carry natural collections of interpretable features
  -- for example, "there is a knight on F3" -- which we leverage into
  supervised metrics for SAE quality. To guide progress in
  interpretable dictionary learning, we introduce a new SAE training technique,
  p-annealing, which improves performance on prior unsupervised
  metrics as well as our new metrics.
paper_ai_summary: Progress in interpretable dictionary learning for language models is evaluated using supervised metrics derived from chess and Othello transcripts, and enhanced by a new training technique called p-annealing.
paper_ai_keywords: sparse autoencoders interpretable features language model representations supervised metrics p-annealing unsupervised metrics