Automatic Domain Adaptation by Transformers in In-Context Learning
<|reference_start|>Automatic Domain Adaptation by Transformers in In-Context Learning: 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.<|reference_end|>
@article{hataya2024automatic,
    title={Automatic Domain Adaptation by Transformers in In-Context Learning},
    author={Ryuichiro Hataya, Kota Matsui, Masaaki Imaizumi},
    journal={arXiv preprint arXiv:2405.16819},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16819},
    primaryClass={cs.LG stat.ML}
}