Effectiveness of Data Augmentation for Prefix Tuning with Limited Data

Recent work has demonstrated that tuning continuous prompts on large, frozen pretrained language models (i.e., prefix tuning or P-tuning) can yield performance that is comparable or superior to fine-tuning. Nevertheless, the effectiveness of such methods under the context of data augmentation, which has been considered a common strategy to improve learning under low data regimes, has not be studied. In this paper, we examine several popular task-agnostic data augmentation techniques, i.e., EDA, Back Translation, and Mixup, when using prefix tuning under data scarcity. We show that data augmentation can be used to boost the performance of prefix tuning models, but the effectiveness of each technique varies and certain methods can lead to a notable degradation in performance, particularly when using larger models and on harder tasks. To help understand the above behaviour, we run experiments which reveal how prefix tuning generally presents a limited ability to separate the sentence embeddings from different classes of augmented data, and displays poorer performance on heavily altered data in particular. We also demonstrate that by adding a simple contrastive loss we can help mitigate such issues for prefix tuning, resulting in an improvement to augmented data performance.