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There is a growing interest in dataset generation recently due to thesuperior generative capacity of large pre-trained language models (PLMs). Inthis paper, we study a flexible and efficient zero-short learning method,\textsc{ZeroGen}. Given a zero-shot task, we first generate a dataset fromscratch using PLMs in an unsupervised manner. Then, we train a tiny task model(e.g., LSTM) under the supervision of the synthesized dataset. This approachallows highly efficient inference as the final task model only has orders ofmagnitude fewer parameters comparing to PLMs (e.g., GPT2-XL). Apart from beingannotation-free and efficient, we argue that \textsc{ZeroGen} can also provideuseful insights from the perspective of data-free model-agnostic knowledgedistillation, and unreferenced text generation evaluation. Experiments andanalysis on different NLP tasks, namely, text classification, questionanswering, and natural language inference, show the effectiveness of\textsc{ZeroGen}.

ZeroGen: Efficient Zero-shot Learning via Dataset Generation