  What are the events involved in a pandemic outbreak? What steps should be
taken when planning a wedding? The answers to these questions can be found by
collecting many documents on the complex event of interest, extracting relevant
information, and analyzing it. We present a new approach in which large
language models are utilized to generate source documents that allow
predicting, given a high-level event definition, the specific events,
arguments, and relations between them to construct a schema that describes the
complex event in its entirety. Using our model, complete schemas on any topic
can be generated on-the-fly without any manual data collection, i.e., in a
zero-shot manner. Moreover, we develop efficient methods to extract pertinent
information from texts and demonstrate in a series of experiments that these
schemas are considered to be more complete than human-curated ones in the
majority of examined scenarios. Finally, we show that this framework is
comparable in performance with previous supervised schema induction methods
that rely on collecting real texts while being more general and flexible
without the need for a predefined ontology.
