Meet ZEROGEN: An Extreme Method for Dataset Generation via PLMs for Zero-Shot Learning

The impressive generative capacity of large-scale pretrained language models (PLMs) has inspired machine learning researchers to explore methods for generating model training examples via PLMs and data augmentation procedures, i.e. dataset generation. A novel contribution in this research direction is proposed in the new paper ZeroGen: Efficient Zero-shot Learning via Dataset Generation, from researchers at the University of Hong Kong, Shanghai AI Lab, Huawei Noah's Ark Lab and the University of Washington. The team describes their proposed ZEROGEN as an "extreme instance" of dataset generation via PLMs for zero-shot learning. ZEROGEN is a framework for prompt-based zero-shot learning (PROMPTING). Unlike existing approaches that rely on gigantic PLMs during inference, ZEROGEM introduces a more flexible and efficient approach for conducting zero-shot learning with PLMs.