Summary of Bam! Just Like That: Simple and Efficient Parameter Upcycling For Mixture Of Experts, by Qizhen Zhang et al.
BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts
by Qizhen Zhang, Nikolas Gritsch, Dwaraknath Gnaneshwar, Simon Guo, David Cairuz, Bharat Venkitesh, Jakob Foerster, Phil Blunsom, Sebastian Ruder, Ahmet Ustun, Acyr Locatelli
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The Mixture of Experts (MoE) framework has gained popularity for large language models due to its superior performance. However, training MoEs from scratch is expensive. Existing methods mitigate this by pre-training multiple dense expert models and using them to initialize an MoE. This method limits the reuse of dense model parameters to only feed-forward network layers. We propose BAM (Branch-Attend-Mix), a simple yet effective method that addresses this shortcoming. BAM uses specialized dense models’ attention parameters fully, initializing soft-MoA layers. We explore two methods for upcycling attention parameters and adopt parallel attention transformer architecture to MoEs. Our experiments demonstrate that BAM surpasses baselines in both perplexity and downstream task performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BAM is a new way to use old language models to make new ones better. This is done by using the good parts of old models, like their attention skills, to help create a new model. The old models are pre-trained on lots of data, which makes them very good at some things. BAM uses these good parts to make a new model that’s even better than before. This helps us solve harder language problems and makes computers more efficient. |
Keywords
» Artificial intelligence » Attention » Mixture of experts » Perplexity » Transformer
