Leveraging QA Datasets to Improve Generative Data Augmentation (2022)

Abstract: The ability of generative language models (GLMs) to generate text has improved considerably in the last few years, enabling their use for generative data augmentation. In this work, we propose CONDA, an approach to further improve GLM’s ability to generate synthetic data by reformulating data generation as context generation for a given question-answer (QA) pair and leveraging QA datasets for training context generators. Then, we cast downstream tasks into the same question answering format and adapt the fine-tuned context generators to the target task domain. Finally, we use the fine-tuned GLM to generate relevant contexts, which are in turn used as synthetic training data for their corresponding tasks. We perform extensive experiments on multiple classification datasets and demonstrate substantial improvements in performance for both few- and zero-shot settings. Our analysis reveals that QA datasets that require high-level reasoning abilities (e.g., abstractive and common-sense QA datasets) tend to give the best boost in performance in both few-shot and zero-shot settings.

## introduction recent advances in nlp have substantially improved the capability of pretrained language models to generate high-quality text @xcite @xcite . various approaches (e.g., @xcite @xcite leverage this capability for generative data augmentation. this process usually involves first fine-tuning the glm on training samples prepended with their target label and then generating synthetic data by prompting the glm with a given target label. however, it is not evident that the model parameters learnt during pretraining or fine-tuning should support * jingbo shang is the corresponding author. ## conda: qa datasets for generative data augmentation in this section, we describe the problem statement, and explain our method including qac fine-tuning, target-domain adaptation, and synthetic training data generation. ## experiments in this section, we evaluate our method against several data augmentation and few-shot methods on sentiment analysis and text classification tasks. ## conclusion in this paper, we propose to train generative language models to be context generators for a given question and answer. to facilitate this, we use question answer as a format and utilize qa datasets for training generative language models into context generators. we view sentiment and topic classification tasks in question-answer form and generate contexts using our fine-tuned generative language models. these generated contexts are used as synthetic training data to augment existing fewshot data for training a classifier. extensive experiments on multiple sentiment and topic classification datasets demonstrate strong performance of our method in few-shot and zero-shot settings.