# UniGen: Universal Domain Generalization for Sentiment Classification via Zero-shot Dataset Generation

**Authors:** Juhwan Choi, Yeonghwa Kim, Seunguk Yu, JungMin Yun, YoungBin Kim  
**arXiv:** [2405.01022](https://arxiv.org/abs/2405.01022)  
**Date:** 2024-05-02  
**Summary page:** https://www.summarizepaper.com/fr/paper/2405.01022v3/

## 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.