Summary of Mixture Of Modular Experts: Distilling Knowledge From a Multilingual Teacher Into Specialized Modular Language Models, by Mohammed Al-maamari et al.
Mixture of Modular Experts: Distilling Knowledge from a Multilingual Teacher into Specialized Modular Language Models
by Mohammed Al-Maamari, Mehdi Ben Amor, Michael Granitzer
First submitted to arxiv on: 28 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 research combines Knowledge Distillation (KD) and Mixture of Experts (MoE) to develop modular, efficient multilingual language models. The study evaluates adaptive versus fixed alpha methods in KD and compares modular MoE architectures for handling multi-domain inputs and preventing catastrophic forgetting. The results show similar performance for both KD methods, with marginal improvements from adaptive alpha. A combined loss approach provides more stable learning. The router, trained to classify input sequences into English, French, German, or Python, achieves 99.95% precision, recall, and F1 score. Evaluations of modular MoE architectures reveal that Pre-trained Language Experts (PLE) and Joint Expert Embedding Training (JEET) perform similarly, while the MoE with Common Expert (MoE-CE) setup shows slightly lower performance. The study also investigates catastrophic forgetting, finding that sequential training leads to significant forgetting, whereas single-session training with balanced batches and the MoE approach mitigates this issue. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research develops a new way to make language models more efficient and work well across multiple languages. They combine two existing techniques: Knowledge Distillation and Mixture of Experts. The study finds that one version of KD works just as well as another, and that a combination approach provides the most stable results. A special router is trained to classify text into different languages, achieving very high accuracy. The researchers also test how well their approach prevents “forgetting” what was learned earlier, finding that it does a good job. Overall, this study shows promise for making language models more efficient and adaptable. |
Keywords
» Artificial intelligence » Embedding » F1 score » Knowledge distillation » Mixture of experts » Precision » Recall
