Features can be used to steer large models.

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• will these methods work for large models?question0.783
  Motivating question for the paper, addressed by scaling SAEs to Claude 3 Sonnet.
• Our method enables bidirectional steering of model behavior.finding0.776
  The method can steer the model in both positive and negative directions on the target semantic.
• Features may not be strictly one-dimensional objects; higher-dimensional feature manifolds may exist in model representationshypothesis0.768
  Extension of superposition hypothesis to account for continuous families of features
• Larger SAEs contain features for concepts not captured in smaller SAEs, indicating improved coverage.claim0.767
  Scaling SAE size increases granularity and discovers new features.
• Base models are good modellers of worlds but not of their own state, because they lack a developed self-model initially.claim0.763
  Observation about asymmetry in base model capabilities.
• Optimally steering model behavior requires isolating concept geometry and defining operators to navigate it.claim0.762
• can we use the feature basis to detect when fine-tuning a model increases the likelihood of undesirable behaviors?question0.762
  Question about practical safety application of feature monitoring.
• Feature steering was effective in 5 out of 7 cases where few-shot probe steering vectors failed to produce meaningful behavior change.finding0.759
  Empirical comparison showing advantage of SAE features in low-data regime.