Summary of Mixture Of Experts Meets Prompt-based Continual Learning, by Minh Le et al.
Mixture of Experts Meets Prompt-Based Continual Learning
by Minh Le, An Nguyen, Huy Nguyen, Trang Nguyen, Trang Pham, Linh Van Ngo, Nhat Ho
First submitted to arxiv on: 23 May 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 This paper explores the power of pre-trained models and prompt-based approaches in continual learning. Unlike other solutions, prompt-based methods excel in preventing catastrophic forgetting with minimal learnable parameters and no memory buffer. While existing methods leverage prompts for state-of-the-art performance, this study provides a theoretical analysis to explain the effectiveness of prompting. The authors reveal that pre-trained models’ attention blocks encode a special mixture of experts architecture, driving the design of a novel gating mechanism called Non-linear Residual Gates (NoRGa). NoRGa enhances continual learning performance while preserving parameter efficiency. Empirical results across diverse benchmarks and pretraining paradigms substantiate the effectiveness of NoRGa. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how computers can learn new things without forgetting what they already know. The researchers found that using special prompts with pre-trained models helps them remember old information better than other methods. They also discovered a secret to making these prompts work well: it’s like adding new experts to help the computer make decisions. This new way of working, called Non-linear Residual Gates (NoRGa), makes computers better at learning new things without forgetting what they already know. |
Keywords
» Artificial intelligence » Attention » Continual learning » Mixture of experts » Pretraining » Prompt » Prompting
