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arXiv · 2405.16819

Automatic Domain Adaptation by Transformers in In-Context Learning

Ryuichiro Hataya·Kota Matsui·Masaaki Imaizumi
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Abstract

Selecting or designing an appropriate domain adaptation algorithm for a given problem remains challenging. This paper presents a Transformer model that can provably approximate and opt for domain adaptation methods for a given dataset in the in-context learning framework, where a foundation model performs new tasks without updating its parameters at test time. Specifically, we prove that Transformers can approximate instance-based and feature-based unsupervised domain adaptation algorithms and automatically select an algorithm suited for a given dataset. Numerical results indicate that in-context learning demonstrates an adaptive domain adaptation surpassing existing methods.

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Ryuichiro Hataya, Kota Matsui, Masaaki Imaizumi. 2024-05-27. Automatic Domain Adaptation by Transformers in In-Context Learning. https://arxiv.org/abs/2405.16819

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