Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet

Scaling Dictionary Learning to Claude 3 Sonnet

1. What question did Scaling Monosemanticity answer about sparse autoencoders?
   Whether dictionary learning, shown to find interpretable features in a tiny one-layer model, scales to a large production model. It does: sparse autoencoders extracted millions of high-quality, often highly abstract features from Claude 3 Sonnet.
   • Templeton et al., 2024, Scaling Monosemanticity (Transformer Circuits Thread), Scaling Dictionary Learning to Claude 3 Sonnet
2. Which two hypotheses underlie the approach of Scaling Monosemanticity?
   The linear representation hypothesis (concepts are represented as directions in activation space) and the superposition hypothesis (almost-orthogonal directions let a network represent more features than it has dimensions).
   • Templeton et al., 2024, Scaling Monosemanticity (Transformer Circuits Thread), Scaling Dictionary Learning to Claude 3 Sonnet
3. Where in Claude 3 Sonnet were the sparse autoencoders applied, and why there?
   On the residual stream at the middle layer: the residual stream is smaller than the MLP layer (cheaper), may reduce cross-layer superposition, and the middle of the model was expected to hold abstract features.
   • Templeton et al., 2024, Scaling Monosemanticity (Transformer Circuits Thread), Scaling Dictionary Learning to Claude 3 Sonnet
4. How large were the sparse autoencoders, and how well did they work?
   About 1M, 4M and 34M features; fewer than 300 features were active per token on average, and reconstructions explained at least 65% of activation variance. Dead features rose with size: about 2%, 35% and 65%.
   • Templeton et al., 2024, Scaling Monosemanticity (Transformer Circuits Thread), Scaling Dictionary Learning to Claude 3 Sonnet
5. How were scaling laws used to train the sparse autoencoders?
   Treating the training loss (reconstruction error plus L1 penalty) as a proxy for quality, sweeps showed the compute-optimal number of features and of training steps both grow roughly as power laws in compute; these trends, and a power law for the learning rate, set the 34M run.
   • Templeton et al., 2024, Scaling Monosemanticity (Transformer Circuits Thread), Scaling Dictionary Learning to Claude 3 Sonnet