Recent studies show that prompt tuning can better leverage the power of large language models than fine-tuning on downstream natural language understanding tasks. However, the existing prompt tuning methods have training instability issues, as the variance of scores under different random seeds is quite large. To address this critical problem, we first investigate and find that the loss landscape of vanilla prompt tuning is precipitous when it is visualized, where a slight change of input data can cause a big fluctuation in the loss landscape. This is an essential factor that leads to the instability of prompt tuning. Based on this observation, we introduce perturbation-based regularizers, which can smooth the loss landscape, into prompt tuning. We propose a new algorithm, called Prompt Tuning with Perturbation-based regularizer~(PTP), which can not only alleviate training instability dramatically but also boost the performance of prompt tuning. We design two kinds of perturbation-based regularizers, including random-noise-based and adversarial-based. In particular, our proposed perturbations are flexible on both text space and embedding space. Extensive experiments show the effectiveness of our proposed methods in stabilizing the training. Our new algorithms improve the state-of-the-art prompt tuning methods by 1.94\% and 2.34\% on SuperGLUE and FewGLUE benchmarks, respectively.

研究表明，在自然语言理解任务中，prompt tuning相比下游微调能更好地利用大型语言模型的优势，但是现有的prompt tuning方法在训练时存在不稳定性问题。本文提出了基于扰动的正则化方法，将其应用到prompt tuning中，从而稳定训练并提高准确性。实验结果表明，本文提出的新方法在SuperGLUE和FewGLUE基准测试中分别比现有状态-of-the-art方法提高了1.94％和2.34％。

PTP：基于扰动正则化的Prompt Tuning提升稳定性和性能