  Prompt-based learning has shown considerable promise in reformulating various
downstream tasks as cloze problems by combining original input with a
predetermined template. This approach demonstrates its effectiveness,
especially in few-shot learning scenarios, where the model is trained on a
scarce amount of data. Despite its successes, the limited templates and text in
few-shot prompt-based learning scenarios leave significant room for performance
improvement. Moreover, existing methods sometimes resort to model ensembles,
which, while effective, could potentially hamper model efficiency due to
increased computational demands. To address these issues, we introduce MixPro,
an augmentation method designed to augment both the vanilla input text and the
templates. We implement this through the token-level, the sentence-level, and
the template-level Mixup strategies. The experimental results on five few-shot
datasets show that MixPro outperforms other augmentation baselines, improving
model performance by an average of 5.08% compared to before augmentation.
