PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer

Recent studies show that prompt tuning can better leverage the power of large language models than fine-tuning on downstream natural language understanding tasks. Nonetheless, current prompt tuning methods encounter instability during training, marked by a high variance in scores given different random seeds. In addressing this crucial issue, we uncover that the loss landscape of standard prompt tuning, when visualized, is remarkably steep, i.e., minor alterations in the input data can trigger substantial fluctuations in the loss landscape, which is an essential factor that leads to the training instability. In light of this finding, we incorporate perturbation-based regularizers to temper the loss landscape within the prompt tuning process. We thus present a novel algorithm, called Prompt Tuning with Perturbation-based regularizer (PTP), that can significantly reduce training instability and concurrently enhance the performance of prompt tuning. Specifically, we design two variants of perturbation-based regularizers: one that employs random noise, and another that uses an adversarial approach. Importantly, our proposed perturbations display flexibility in both the text and embedding spaces. 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.