Towards Monosemanticity: Decomposing Language Models With Dictionary Learning
Bricken et al., 2023, Towards Monosemanticity (Transformer Circuits Thread)
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Future Work
- What is the main obstacle to applying this method to frontier models?Cost: a 100× expansion of one 10,000-wide MLP layer needs an autoencoder of about 20 billion parameters, possibly trained on much of the model’s corpus to catch rare features; better metrics for judging feature quality are also missing.
- Bricken et al., 2023, Towards Monosemanticity (Transformer Circuits Thread), Future Work
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