Summary of Glider: Global and Local Instruction-driven Expert Router, by Pingzhi Li et al.
Glider: Global and Local Instruction-Driven Expert Router
by Pingzhi Li, Prateek Yadav, Jaehong Yoon, Jie Peng, Yi-Lin Sung, Mohit Bansal, Tianlong Chen
First submitted to arxiv on: 9 Oct 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 paper proposes a novel approach to model moerging, which combines the strengths of pre-trained models and expert modules to create more powerful and adaptive systems. The existing methods prioritize generalization over performance on held-in tasks, limiting their practical applicability. To address this, the authors introduce GLIDER (Global and Local Instruction Driven Expert Router), a multi-scale routing mechanism that incorporates both semantic global and local information. This allows for more effective expert selection and improved performance on unseen tasks. The proposed approach is tested using T5-based models on T0 and FLAN tasks, demonstrating substantial improvements in held-in performance while maintaining strong generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us create better computer systems that can learn from experience and adapt to new situations. Currently, these systems prioritize doing well on one task over another. The authors develop a new approach called GLIDER that takes into account the bigger picture of what we want to achieve. This allows for better decisions when choosing which experts to use in different situations. The results show that this approach leads to better performance and generalization. |
Keywords
» Artificial intelligence » Generalization » T5
