Monet: Mixture of Monosemantic Experts for Transformers Explained — LessWrong
Note: This is an exposition of the recent preprint "Monet: Mixture of Monosemantic Experts for Transformers". I wrote this exposition as my project for the ARBOx program, I was not involved in writing the paper. Any errors are my own. Thank you to @David Quarel for his excellent comments and suggestions. TL;DR: MONET is a novel neural network architecture that achieves interpretability by design rather than through post-hoc analysis. Using a specialized Mixture of Experts (MoE) architecture with ~250k experts per layer, MONET encourages individual components to learn specific, interpretable tasks (e.g., Python coding, biology knowledge, or toxicity generation). This enables selective removal of capabilities without harming performance in other domains. I show that MONET can be framed as a standard transformer MLP with two key modifications: (1) forced sparse activations in the hidden layer and (2) dynamic reshuffling of the hidden layer. This interpretation helps explain how MONET achi
x Monet: Mixture of Monosemantic Experts for Transformers Explained — LessWrong Academic Papers Interpretability (ML & AI) Transformers AI Frontpage 31 Monet: Mixture of Monosemantic Experts for Transformers Explained by CalebMaresca 25th Jan 2025 13 min read 2 31 Note: This is an exposition of the recent preprint " Monet: Mixture of Monosemantic Experts for Transformers ". I wrote this exposition as my project for the ARBOx program , I was not involved in writing the paper. Any errors are my own. Thank you to @David Quarel for his excellent comments and suggestions. TL;DR: MONET is a novel ne
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