The prompt has become an effective linguistic tool for utilizing pre-trained language models. However, in few-shot scenarios, subtle changes in the prompt design always make the result widely different, and the prompt learning methods also make it easy to overfit the limited samples. To alleviate this, we explore utilizing suitable contrastive samples and multi-degree contrastive learning methods to improve the robustness of the prompt representation. Therefore, the proposed Consprompt combined with the prompt encoding network, contrastive sampling modules, and contrastive scoring modules, is introduced to realize differential contrastive learning. Our results exhibit state-of-the-art performance in different few-shot settings, and the ablation experiments also certify the effectiveness of utilizing multi-degree contrastive learning in the prompt-based fine-tuning process.

翻译：提示已成为利用预训练语言模型的有效语言工具。然而，在少样本场景中，提示设计的细微变化常导致结果产生巨大差异，且提示学习方法易对有限样本产生过拟合。为解决此问题，我们探索利用合适的对比样本及多程度对比学习方法提升提示表示的鲁棒性。因此，本文提出的ConsPrompt方法结合提示编码网络、对比采样模块与对比评分模块，实现差异化对比学习。实验结果表明，该方法在多种少样本设定下均取得最先进性能，消融实验也证实了在基于提示的微调过程中采用多程度对比学习的有效性。