Prompt-based learning reformulates downstream tasks as cloze problems by combining the original input with a template. This technique is particularly useful in few-shot learning, where a model is trained on a limited amount of data. However, the limited templates and text used in few-shot prompt-based learning still leave significant room for performance improvement. Additionally, existing methods using model ensembles can constrain the model efficiency. To address these issues, we propose an augmentation method called MixPro, which augments both the vanilla input text and the templates through token-level, sentence-level, and epoch-level Mixup strategies. We conduct experiments on five few-shot datasets, and the results show that MixPro outperforms other augmentation baselines, improving model performance by an average of 5.08% compared to before augmentation.

翻译：提示学习通过将原始输入与模板相结合，将下游任务重构为完形填空问题。该技术在小样本学习场景中尤为有效，此时模型仅基于有限数据进行训练。然而，小样本提示学习中使用的有限模板和文本仍存在显著的性能提升空间。此外，现有方法采用模型集成会制约模型效率。为解决这些问题，我们提出一种名为MixPro的增强方法，通过词元级、句子级和轮次级混合策略，同时对原始输入文本和模板进行增强。我们在五个小样本数据集上开展实验，结果表明MixPro优于其他增强基线方法，与增强前相比，模型性能平均提升5.08%。