Summary of Learn It or Leave It: Module Composition and Pruning For Continual Learning, by Mingyang Wang et al.
Learn it or Leave it: Module Composition and Pruning for Continual Learning
by Mingyang Wang, Heike Adel, Lukas Lange, Jannik Strötgen, Hinrich Schütze
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 MoCL-P is a novel lightweight continual learning method designed to tackle the challenges of applying pretrained language models to real-world environments. The approach addresses issues like catastrophic forgetting, knowledge transfer, and parameter efficiency by integrating task representation-guided module composition with adaptive pruning. MoCL-P achieves state-of-the-art performance on three continual learning benchmarks, showcasing its potential for practical applications where resource constraints are a concern. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoCL-P is a new way to make language models learn new things without forgetting what they already know. This helps with tasks like chatbots that need to talk about different topics. The problem is that big language models get worse if you add too many new tasks, or “tasks” in AI speak. MoCL-P solves this by being smart about what it learns and how it uses its old knowledge. It even makes the model more efficient so it doesn’t take up too much computer power. |
Keywords
» Artificial intelligence » Continual learning » Pruning
