Efforts to leverage deep learning models in low-resource regimes have led to numerous augmentation studies. Inspired by recent research on decision boundaries, this paper proposes a decision-boundary-aware data augmentation strategy to enhance robustness using pre-trained language models. Additionally, mid-K sampling is suggested to increase the diversity of generated sentences. The paper demonstrates the performance of the proposed augmentation strategy in comparison to other methods through extensive experiments.