Summary of Pmoe: Progressive Mixture Of Experts with Asymmetric Transformer For Continual Learning, by Min Jae Jung et al.
PMoE: Progressive Mixture of Experts with Asymmetric Transformer for Continual Learning
by Min Jae Jung, JooHee Kim
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 proposed Progressive Mixture of Experts with Asymmetric Transformer (PMoE) addresses the issue of catastrophic forgetting in Large Language Models (LLMs), enabling them to learn continuously without losing previously acquired knowledge. By utilizing an asymmetric design, PMoE dedicates shallow layers to general knowledge and deep layers for new information. This approach incorporates progressively added experts in deep layers and a router that efficiently allocates new knowledge to the appropriate experts. Experimental results on TRACE datasets and general language understanding benchmarks show that PMoE outperforms previous state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a way for large language models to keep learning without forgetting what they already know. This is called “continual learning” and it’s important because it helps us use machines more efficiently and reduce waste. The new model, called PMoE, uses a special design that keeps general knowledge in one part and new information in another. It also has a “router” that helps figure out where to put the new information. This allows the model to learn continuously without losing its old knowledge. Tests showed that PMoE works better than other models at doing this. |
Keywords
» Artificial intelligence » Continual learning » Language understanding » Mixture of experts » Transformer
