Summary of Learning to Route For Dynamic Adapter Composition in Continual Learning with Language Models, by Vladimir Araujo et al.
Learning to Route for Dynamic Adapter Composition in Continual Learning with Language Models
by Vladimir Araujo, Marie-Francine Moens, Tinne Tuytelaars
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 research paper presents a novel approach called L2R for fine-tuning pre-trained language models (PLMs) in continual learning (CL). The method, parameter-efficient fine-tuning (PEFT), is widely used but faces limitations during module training and composition. L2R isolates new PEFT module training to ensure task specialization and develops a router network that learns from a memory of previously seen tasks. We evaluate L2R on two CL setups using various benchmarks, showing improved generalization and performance compared to other methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem with fine-tuning language models for new tasks while learning many things at once. The usual way to do this has some flaws. This new method, called L2R, makes sure the new modules learn only what they need to know and helps them work well together. We tested it on different types of tasks and showed that it works better than other methods. |
Keywords
» Artificial intelligence » Continual learning » Fine tuning » Generalization » Parameter efficient
