Summary of Rehearsal-free Modular and Compositional Continual Learning For Language Models, by Mingyang Wang et al.
Rehearsal-Free Modular and Compositional Continual Learning for Language Models
by Mingyang Wang, Heike Adel, Lukas Lange, Jannik Strötgen, Hinrich Schütze
First submitted to arxiv on: 31 Mar 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 This paper proposes a novel approach to continual learning, addressing the issue of catastrophic forgetting in machine learning. The authors introduce MoCL, a Modular and Compositional Continual Learning framework that does not require storing previous data or isolating parameters for each task. Instead, MoCL adds new modules to language models and composes them with existing ones, facilitating knowledge transfer between tasks. The approach is evaluated on various benchmarks, outperforming state-of-the-art methods and demonstrating effective transfer of learned knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoCL is a new way for computers to learn new things without forgetting what they already know. Right now, there are two main ways to do this: one stores old information so it can be used again, and the other separates different tasks into separate groups. But these methods have problems – storing old info takes up too much space and memory, while separating tasks doesn’t let them help each other. MoCL is a new approach that lets computers add new ideas to what they already know without needing extra storage or separation. It works by adding new “modules” to language models (like special tools for understanding language) and combining them with old ones. The results show that MoCL does better than current methods at sharing knowledge between tasks. |
Keywords
* Artificial intelligence * Continual learning * Machine learning
