Towards Monosemanticity: Decomposing Language Models With Dictionary Learning
Bricken et al., 2023, Towards Monosemanticity (Transformer Circuits Thread)
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Superposition Hypothesis
- Why should the dictionary have more features than neurons?The superposition hypothesis: networks represent more features than they have neurons by exploiting sparsity, approximately simulating a much larger, sparser network. So feature directions form an overcomplete basis with sparse activations, the classic dictionary-learning problem.
- Bricken et al., 2023, Towards Monosemanticity (Transformer Circuits Thread), Superposition Hypothesis
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