Summary of Monet: Mixture Of Monosemantic Experts For Transformers, by Jungwoo Park et al.
Monet: Mixture of Monosemantic Experts for Transformers
by Jungwoo Park, Young Jin Ahn, Kee-Eung Kim, Jaewoo Kang
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel architecture, Mixture of Monosemantic Experts for Transformers (Monet), to improve the interpretability of large language models (LLMs). The main challenge is polysemanticity, where individual neurons respond to multiple concepts. Previous attempts to disentangle these features using Sparse Autoencoders (SAEs) compromised LLM performance due to reliance on post-hoc reconstruction loss. Monet incorporates sparse dictionary learning directly into end-to-end Mixture-of-Experts pretraining, enabling scaling of expert counts while maintaining overall model performance. The paper demonstrates mutual exclusivity of knowledge across experts and showcases the parametric knowledge encapsulated within individual experts. Additionally, Monet allows for knowledge manipulation over domains, languages, and toxicity mitigation without degrading general performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making large language models more understandable and less likely to create toxic content. Right now, it’s hard to know what a neuron in these models is actually doing because each neuron responds to many different things. Previous attempts to fix this problem have made the models worse at their main job. The new architecture, Monet, fixes this by directly incorporating a special type of dictionary learning into the model’s training process. This allows for much more expertise within the model without making it worse overall. The paper shows that these separate experts don’t duplicate each other’s knowledge and can even be used to make the model better at specific tasks like not creating toxic content. |
Keywords
» Artificial intelligence » Mixture of experts » Pretraining
