Summary of Mixture Of Diverse Size Experts, by Manxi Sun et al.
Mixture of Diverse Size Experts
by Manxi Sun, Wei Liu, Jian Luan, Pengzhi Gao, Bin Wang
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Sparsely-Activated Mixture-of-Experts (MoE) has gained popularity for scaling up large language models without exploding computational costs. The current design faces a challenge where all experts have the same size, limiting token choice for generating the next token. This paper proposes the Mixture of Diverse Size Experts (MoDSE), an MoE architecture with layers featuring experts of different sizes. Analysis shows that diverse-sized experts achieve better predictions and stable routing paths after training. However, this approach can lead to uneven workload distribution. To address this, the authors introduce an expert-pair allocation strategy for even workload distribution across multiple GPUs. Evaluations on multiple benchmarks demonstrate MoDSE’s effectiveness, outperforming existing MoEs by adapting parameter budgets while maintaining the same total parameter size and number of experts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoDSE is a new way to make language models work better without using too much computer power. It lets different “experts” have different sizes, which helps the model choose the right expert for each task. This makes predictions more accurate and stable. However, it can also cause some experts to do more work than others. To fix this, MoDSE uses a special strategy to make sure all experts get an equal share of work. Overall, MoDSE does better than other models on lots of different tasks. |
Keywords
» Artificial intelligence » Mixture of experts » Token
