Amid ongoing health crisis, there is a growing necessity to discern possible signs of Wellness Dimensions (WD) manifested in self-narrated text. As the distribution of WD on social media data is intrinsically imbalanced, we experiment the generative NLP models for data augmentation to enable further improvement in the pre-screening task of classifying WD. To this end, we propose a simple yet effective data augmentation approach through prompt-based Generative NLP models, and evaluate the ROUGE scores and syntactic/semantic similarity among existing interpretations and augmented data. Our approach with ChatGPT model surpasses all the other methods and achieves improvement over baselines such as Easy-Data Augmentation and Backtranslation. Introducing data augmentation to generate more training samples and balanced dataset, results in the improved F-score and the Matthew's Correlation Coefficient for upto 13.11% and 15.95%, respectively.

翻译：在当前持续的健康危机中，辨别自我叙述文本中可能体现的健康维度（WD）迹象的需求日益增长。由于社交媒体数据中WD的分布本质上是失衡的，我们实验了生成式NLP模型用于数据增强，以进一步改进WD分类的预筛选任务。为此，我们提出了一种简单有效的数据增强方法，即通过基于提示的生成式NLP模型，并评估了现有解释与增强数据之间的ROUGE得分及句法/语义相似度。我们的ChatGPT模型方法优于所有其他方法，并在Easy-Data Augmentation和Backtranslation等基线上取得了改进。引入数据增强以生成更多训练样本和平衡数据集，使得F分数和马修斯相关系数分别提升了最高13.11%和15.95%。