G-DAug: Generative Data Augmentation for Commonsense Reasoning
Table 5: Comparison of different training strategies on WinoGrande-L. G-DAug’s two-stage training achieves the highest accuracy.
['Method', 'Accuracy']
aimed at mitigating effects of noise in the generated data. Mixing refers to simply training on the union of the generated and original data, and importance-weighted loss is similar but downweights the generated examples such that their aggregate weight in the loss computation equals that of the original data. G-DAug’s staged training approach is shown to outperform the other methods on this data set.