Towards Monosemanticity: Decomposing Language Models With Dictionary Learning
Bricken et al., 2023, Towards Monosemanticity (Transformer Circuits Thread)
Subscriber deck — free during early access
1 cards in “Using Sparse Autoencoders To Find Good Decompositions” · whole deck
Using Sparse Autoencoders To Find Good Decompositions
- Why use a sparse autoencoder rather than classic dictionary-learning algorithms?It scales to huge datasets (8 billion activation vectors here), and it has roughly the power of an MLP layer, whereas exact sparse recovery (compressed sensing) is NP-hard and might find “features” the model itself cannot use.
- Bricken et al., 2023, Towards Monosemanticity (Transformer Circuits Thread), Using Sparse Autoencoders To Find Good Decompositions
Source
- Trenton Bricken, Adly Templeton, Joshua Batson, Brian Chen, Adam Jermyn, Tom Conerly, Nicholas L. Turner, Cem Anil, Carson Denison, Amanda Askell, Robert Lasenby, Yifan Wu, Shauna Kravec, Nicholas Schiefer, Tim Maxwell, Nicholas Joseph, Zac Hatfield-Dodds, Alex Tamkin, Karina Nguyen, Brayden McLean, Josiah E. Burke, Tristan Hume, Shan Carter, Tom Henighan and Chris Olah. 2023. Towards Monosemanticity: Decomposing Language Models With Dictionary Learning. In Transformer Circuits Thread. https://transformer-circuits.pub/2023/monosemantic-features/index.html