Summary of Adapters Strike Back, by Jan-martin O. Steitz and Stefan Roth
Adapters Strike Back
by Jan-Martin O. Steitz, Stefan Roth
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 presents an in-depth study of adapters, a lightweight mechanism for adapting trained transformer models to various tasks. While adapters have been shown to be outperformed by other adaptation mechanisms like low-rank adaptation, the authors suggest an improved adapter architecture called Adapter+, which not only surpasses previous adapter implementations but also several more complex adaptation mechanisms in challenging settings. The suggested adapter is highly robust and requires little manual intervention when addressing a novel scenario. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Adapters are a way to make trained models work on different tasks without having to retrain them from scratch. But adapters haven’t always been the best option, especially compared to low-rank adaptation. In this paper, the authors take a closer look at how adapters work and what makes them tick. They even suggest a new type of adapter that’s really good and doesn’t need much extra help when trying something new. |
Keywords
» Artificial intelligence » Low rank adaptation » Transformer
