@inproceedings{choi-etal-2024-unigen,
    title = "{U}ni{G}en: Universal Domain Generalization for Sentiment Classification via Zero-shot Dataset Generation",
    author = "Choi, Juhwan  and
      Kim, Yeonghwa  and
      Yu, Seunguk  and
      Yun, JungMin  and
      Kim, YoungBin",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/icon-24-ingestion/2024.emnlp-main.1/",
    doi = "10.18653/v1/2024.emnlp-main.1",
    pages = "1--14",
    abstract = "Although pre-trained language models have exhibited great flexibility and versatility with prompt-based few-shot learning, they suffer from the extensive parameter size and limited applicability for inference. Recent studies have suggested that PLMs be used as dataset generators and a tiny task-specific model be trained to achieve efficient inference. However, their applicability to various domains is limited because they tend to generate domain-specific datasets. In this work, we propose a novel approach to universal domain generalization that generates a dataset regardless of the target domain. This allows for generalization of the tiny task model to any domain that shares the label space, thus enhancing the real-world applicability of the dataset generation paradigm. Our experiments indicate that the proposed method accomplishes generalizability across various domains while using a parameter set that is orders of magnitude smaller than PLMs."
}