Prompt Consistency for Zero-Shot Task Generalization

One of the most impressive results of recent NLP history is the ability of pre-trained language models to solve new tasks in a zeroshot setting.To achieve this, NLP tasks are framed as natural language prompts, generating a response indicating the predicted output.Nonetheless, the performance in such settings often lags far behind its supervised counterpart, suggesting a large space for potential improvement.In this paper, we explore methods to utilize unlabeled data to improve zero-shot performance.Specifically, we take advantage of the fact that multiple prompts can be used to specify a single task, and propose to regularize prompt consistency, encouraging consistent predictions over this diverse set of prompts.Our method makes it possible to fine-tune the model either with extra unlabeled training data, or directly on test input at inference time in an unsupervised manner.In experiments, our approach outperforms the state-of-the-art zeroshot learner, T0 (Sanh et al., 2022), on 9 out of 11 datasets across 4 NLP tasks by up to 10.6 absolute points in terms of accuracy.The gains are often attained with a small number of unlabeled examples.1 * Equal contribution.Order determined by swapping the one in He et al. (2022). 1 Code is available at https://github.com/violet-zct/swarm- distillation-zero-shot.