Event schemas are a form of world knowledge about the typical progression of events. Recent methods for event schema induction use information extraction systems to construct a large number of event graph instances from documents, and then learn to generalize the schema from such instances. In contrast, we propose to treat event schemas as a form of commonsense knowledge that can be derived from large language models (LLMs). This new paradigm greatly simplifies the schema induction process and allows us to handle both hierarchical relations and temporal relations between events in a straightforward way. Since event schemas have complex graph structures, we design an incremental prompting and verification method to break down the construction of a complex event graph into three stages: event skeleton construction, event expansion, and event-event relation verification. Compared to directly using LLMs to generate a linearized graph, our method can generate large and complex schemas with 7.2% F1 improvement in temporal relations and 31.0% F1 improvement in hierarchical relations. In addition, compared to the previous state-of-the-art closed-domain schema induction model, human assessors were able to cover $\sim$10% more events when translating the schemas into coherent stories and rated our schemas 1.3 points higher (on a 5-point scale) in terms of readability.

翻译：事件模式是关于事件典型演进过程的一种世界知识。现有的事件模式归纳方法利用信息抽取系统从文档中构建大量事件图实例，随后学习从这类实例中泛化出模式。与此不同，我们提出将事件模式视为可从大型语言模型（LLMs）推导出的常识知识形式。这一全新范式极大简化了模式归纳流程，并能以直接方式处理事件间的层次关系与时间关系。鉴于事件模式具有复杂的图结构，我们设计了一种增量提示与验证方法，将复杂事件图的构建分解为三个阶段：事件骨架构建、事件扩展和事件间关系验证。与直接使用LLMs生成线性化图结构相比，我们的方法能生成大规模复杂模式，在时间关系上F1值提升7.2%，在层次关系上F1值提升31.0%。此外，相较于此前最先进的封闭域模式归纳模型，人类评估者在将模式转换为连贯故事时能够覆盖多约10%的事件，并从可读性角度（5分制）对我们的模式给出了1.3分更高的评分。