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.

本研究提出了一种新颖的方法，通过生成适用于任意领域的数据集来实现小型任务模型的领域通用性，从而提高数据集生成模式的实际应用性。实验证明，该方法在使用比预训练语言模型小数个数量级的参数集的情况下，实现了在各种领域的泛化能力。