G-DAug: Generative Data Augmentation for Commonsense Reasoning
Table 7: Results comparing G-DAug’s filtering methods against using entire synthetic data pool for augmentation, on WinoGrande-M. The synthetic data sizes are shown in parentheses. The selection algorithms achieve comparable or better performance despite training on three times fewer synthetic examples.
['Synthetic Data', 'Accuracy']
G-DAug ’s filtering methods are designed to identify a high-quality and diverse subset of the generated data, in order to reduce the training cost compared to training on the entire generated pool—without harming accuracy. The selection approaches provide comparable or better accuracy compared to using the whole pool, despite using 3 times less synthetic training data.