Summary of Automatic Domain Adaptation by Transformers in In-context Learning, By Ryuichiro Hataya et al.
Automatic Domain Adaptation by Transformers in In-Context Learning
by Ryuichiro Hataya, Kota Matsui, Masaaki Imaizumi
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 The paper presents a Transformer model that can provably approximate and choose the most suitable domain adaptation method for a specific dataset within the context of in-context learning, where a foundation model performs new tasks without updating its parameters at test time. The model can approximate instance-based and feature-based unsupervised domain adaptation algorithms, automatically selecting an algorithm suited for a given dataset. Numerical results show that in-context learning achieves adaptive domain adaptation outperforming existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve the problem of choosing the right domain adaptation algorithm by creating a Transformer model that can learn and adapt to new tasks without updating its parameters. The model is designed for “in-context learning,” where it can perform new tasks while keeping its original training data intact. This approach has been shown to be effective in adapting to different datasets and performing better than other methods. |
Keywords
» Artificial intelligence » Domain adaptation » Transformer » Unsupervised
