Convert Cross-Domain Classification Into Few-Shot Learning: A Unified Prompt-Tuning Framework for Unsupervised Domain Adaptation

Unsupervised domain adaptation aims to facilitate learning tasks within an unlabeled target domain by leveraging labeled information from the source domain. To address the domain discrepancy, the majority of existing methods focus on learning latent features in both the source and target domains. However, the essential challenge in unsupervised domain adaptation lies in locating labeled information, specifically the true labels of the data, within the target domain. In this paper, we propose a unified prompt-tuning framework that converts cross-domain classification into few-shot learning, effectively integrating diverse cross-domain classification tasks into an iterative few-shot learning paradigm. The framework unifies all predicted pseudo labels, templates, and iterated models into a cohesive prompt-tuning model, exhibiting robust scalability for integrating other tuning modalities and offering high availability to minimize the necessity for extensive fine-tuning. Through extensive experiments on several well-known benchmark datasets, we validate the superior performance of our framework compared to other domain adaptation and fine-tuning PLMs methods, which achieves up to an impressive 99% predicting accuracy of true labels within the target domain.