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Recently, dataset-generation-based zero-shot learning has shown promisingresults by training a task-specific model with a dataset synthesized from largepre-trained language models (PLMs). The final task-specific model oftenachieves compatible or even better performance than PLMs under the zero-shotsetting, with orders of magnitude fewer parameters. However, synthetic datasetshave their drawbacks. They have long been suffering from low-quality issues(e.g., low informativeness and redundancy). This explains why the massivesynthetic data does not lead to better performance -- a scenario we wouldexpect in the human-labeled data. To improve the quality of dataset synthesis,we propose a progressive zero-shot dataset generation framework, ProGen, whichleverages the feedback from the task-specific model to guide the generation ofnew training data via in-context examples. Extensive experiments on five textclassification datasets demonstrate the effectiveness of the proposed approach.We also show ProGen achieves on-par or superior performance with only 1\%synthetic dataset size compared to baseline methods without in-contextfeedback.

ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback