UniGen: Universal Domain Generalization for Sentiment Classification via Zero-shot Dataset Generation
<|reference_start|>UniGen: Universal Domain Generalization for Sentiment Classification via Zero-shot Dataset Generation: 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.<|reference_end|>
@article{choi2024unigen:,
    title={UniGen: Universal Domain Generalization for Sentiment Classification via
  Zero-shot Dataset Generation},
    author={Juhwan Choi, Yeonghwa Kim, Seunguk Yu, JungMin Yun, YoungBin Kim},
    journal={arXiv preprint arXiv:2405.01022},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01022},
    primaryClass={cs.CL cs.AI}
}