Prompt Consistency for Zero-Shot Task Generalization

Understand

One of the most impressive results of recent NLP history is the ability of pre-trained language models to solve new tasks in a zero-shot 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.