Schema induction builds a graph representation explaining how events unfold in a scenario. Existing approaches have been based on information retrieval (IR) and information extraction(IE), often with limited human curation. We demonstrate a human-in-the-loop schema induction system powered by GPT-3. We first describe the different modules of our system, including prompting to generate schematic elements, manual edit of those elements, and conversion of those into a schema graph. By qualitatively comparing our system to previous ones, we show that our system not only transfers to new domains more easily than previous approaches, but also reduces efforts of human curation thanks to our interactive interface.

翻译：模式归纳构建了一种图表示，用以解释特定场景中事件是如何展开的。现有方法主要基于信息检索（IR）与信息抽取（IE），且常常依赖有限的人工修正。我们展示了一个由GPT-3驱动的人机协同模式归纳系统。首先，我们描述了系统的各个模块，包括通过提示生成模式元素、对这些元素进行手动编辑，以及将其转换为模式图。通过将我们的系统与以往方法进行定性比较，我们证明该系统不仅能比以往方法更容易地迁移到新领域，而且由于采用交互式界面，还减少了人工修正的工作量。