Prompt tuning is a parameter-efficient method, which learns soft prompts and conditions frozen language models to perform specific downstream tasks. Though effective, prompt tuning under few-shot settings on the one hand heavily relies on a good initialization of soft prompts. On the other hand, it can easily result in overfitting. Existing works leverage pre-training or supervised meta-learning to initialize soft prompts but they cannot data-efficiently generalize to unseen downstream tasks. To address the above problems, this paper proposes a novel Self-sUpervised meta-Prompt learning framework with meta-gradient Regularization for few-shot generalization (SUPMER). We first design a set of self-supervised anchor meta-training tasks with different task formats and further enrich the task distribution with curriculum-based task augmentation. Then a novel meta-gradient regularization method is integrated into meta-prompt learning. It meta-learns to transform the raw gradients during few-shot learning into a domain-generalizable direction, thus alleviating the problem of overfitting. Extensive experiments show that SUPMER achieves better performance for different few-shot downstream tasks, and also exhibits a stronger domain generalization ability.

翻译：[翻译摘要] 提示调优是一种参数高效的方法，通过学习软提示并调控冻结语言模型来执行特定下游任务。尽管有效，但在小样本设置下的提示调优一方面高度依赖软提示的良好初始化，另一方面容易导致过拟合。现有工作利用预训练或监督元学习来初始化软提示，但无法以数据高效方式泛化到未见过的下游任务。为解决上述问题，本文提出一种新颖的自监督元提示学习框架，结合元梯度正则化实现小样本泛化（SUPMER）。我们首先设计一组具有不同任务格式的自监督锚定元训练任务，并通过基于课程的任务增强进一步丰富任务分布。随后，一种新颖的元梯度正则化方法被集成到元提示学习中，该方法通过元学习将小样本学习过程中的原始梯度转换为领域可泛化的方向，从而缓解过拟合问题。大量实验表明，SUPMER在不同小样本下游任务中均能取得更优性能，并展现出更强的领域泛化能力。