Zero-shot cross-domain slot filling aims to transfer knowledge from the
labeled source domain to the unlabeled target domain. Existing models either
encode slot descriptions and examples or design handcrafted question templates
using heuristic rules, suffering from poor generalization capability or
robustness. In this paper, we propose a generative zero-shot prompt learning
framework for cross-domain slot filling, both improving generalization and
robustness than previous work. Besides, we introduce a novel inverse prompting
strategy to distinguish different slot types to avoid the multiple prediction
problem, and an efficient prompt-tuning strategy to boost higher performance by
only training fewer prompt parameters. Experiments and analysis demonstrate the
effectiveness of our proposed framework, especially huge improvements (+13.44%
F1) on the unseen slots.

零 - shot 跨领域槽填充的生成式零 - shot 提示学习框架，通过反向提示策略区分不同槽类型以避免多重预测问题，以及通过训练更少的提示参数来提高性能，证明了我们提出的框架的有效性，尤其是在未见过的槽上有巨大的提升（+13.44% F1）。