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arXiv · 2211.04668

Zero-Label Prompt Selection

Chonghua Liao·Yanan Zheng·Zhilin Yang
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Abstract

Natural language prompts have been shown to facilitate cross-task generalization for large language models. However, with no or limited labeled examples, the cross-task performance is highly sensitive to the choice of prompts, while selecting a high-performing prompt is challenging given the scarcity of labels. To address the issue, we propose a Zero-Label Prompt Selection (ZPS) method that selects prompts without any labeled data or gradient update. Specifically, given the candidate human-written prompts for a task, ZPS labels a set of unlabeled data with a prompt ensemble and uses the pseudo-labels for prompt selection. Experiments show that ZPS improves over prior methods by a sizeable margin in zero-label performance. We also extend ZPS to a few-shot setting and show its advantages over strong baselines such as prompt tuning and model tuning.

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Chonghua Liao, Yanan Zheng, Zhilin Yang. 2022-11-09. Zero-Label Prompt Selection. https://arxiv.org/abs/2211.04668

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