An interesting take on zero-shot learning was introduced in a paper that was dated Feb 16. More efficient and flexible ways to conduct zero-shot learning with PLMs were explored by the authors. They take the dataset generation method to the extreme and study ZeroGEN, a flexible and efficient zero-shot learning framework via dataset generation. With the pseudo-dataset, a tiny task model TAM is trained to conduct the given task. This procedure is highly flexible, meaning that any model architecture, loss function, and training strategy can be used.

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.