Summary of Scalable Multi-domain Adaptation Of Language Models Using Modular Experts, by Peter Schafhalter et al.
Scalable Multi-Domain Adaptation of Language Models using Modular Experts
by Peter Schafhalter, Shun Liao, Yanqi Zhou, Chih-Kuan Yeh, Arun Kandoor, James Laudon
First submitted to arxiv on: 14 Oct 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 paper proposes Modular Domain Experts (MoDE), a mixture-of-experts architecture that augments general pre-trained language models with modular, domain-specialized experts. MoDE is designed to balance domain-specific performance, retention of general knowledge, and efficiency for training and inference on resource-constrained devices. The proposed method achieves comparable target performances to full parameter fine-tuning while retaining 1.65% more general knowledge. Additionally, MoDE enables flexible sharding configurations and improves training speeds by up to 38%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoDE is a new way to make language models work better on specific tasks without losing their overall abilities. It’s like adding special tools to a general toolbox to help with specific jobs. This helps devices that don’t have a lot of power or storage to still do important tasks. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Mixture of experts
