ZeroShotDataAug: Generating and Augmenting Training Data with ChatGPT

Data augmentation is a technique to increase the size of the training data available to machine learning models without requiring additional human annotation of data. Increasing the size of training data, provided the additional data is somewhat diverse, is pertinent to enable model generalization especially in low resource tasks. The aim of this paper is to evaluate zero-shot prompting of ChatGPT for data augmentation in the low resource scenario. Wei and Zou [14] proposed Easy Data Augmentation (EDA) which is a technique based on word replacement that includes four types of operations: synonym replacement, random insertion, random deletion, and random swap. In synonym replacement, words with similar meanings are substituted for some of the original words in the text.