VASAE: Naming SAE Dictionary Directions with Vocabulary-Aligned Anchoring
For researchers interpreting SAE features, VASAE provides a direct connection between learned features and token vocabulary, complementing post hoc interpretation.
VASAE trains SAE features under vocabulary-aligned anchoring, assigning each feature an intrinsic token name. It aligns ~90% of features in GPT-2-small layers 0-10 and 92.8% in a shallow Llama-3.1-8B layer without reducing reconstruction quality.
Sparse autoencoders (SAEs) provide useful decompositions of Transformer residual streams, but their learned features are usually named post hoc rather than directly connected to the Transformer's token vocabulary. We introduce Vocabulary-Aligned Sparse Autoencoder (VASAE), a method that trains SAE features under vocabulary-aligned anchoring and assigns each feature an intrinsic token name: the token string whose embedding is nearest to that feature. Without reducing reconstruction quality compared with a standard SAE, VASAE produces dictionaries with vocabulary-aligned features. Using a 0.8 cutoff on the nearest-token alignment score, dictionaries trained on GPT-2-small post-residual streams align about 90% of features in layers 0--10. In Llama-3.1-8B, representative shallow and middle-layer dictionaries contain strongly aligned features, including 92.8% in the shallow layer, while the representative final-layer dictionary shows limited alignment. After subtracting the sentence-level mean sparse code, case studies show that many remaining intrinsic token names are relevant to nearby input tokens. These results suggest that vocabulary-aligned anchoring can connect learned features to intrinsic token names during training, complementing post hoc interpretation of learned dictionaries.