  Past work has studied event prediction and event language modeling, sometimes
mediated through structured representations of knowledge in the form of event
schemas. Such schemas can lead to explainable predictions and forecasting of
unseen events given incomplete information. In this work, we look at the
process of creating such schemas to describe complex events. We use large
language models (LLMs) to draft schemas directly in natural language, which can
be further refined by human curators as necessary. Our focus is on whether we
can achieve sufficient diversity and recall of key events and whether we can
produce the schemas in a sufficiently descriptive style. We show that large
language models are able to achieve moderate recall against schemas taken from
two different datasets, with even better results when multiple prompts and
multiple samples are combined. Moreover, we show that textual entailment
methods can be used for both matching schemas to instances of events as well as
evaluating overlap between gold and predicted schemas. Our method paves the way
for easier distillation of event knowledge from large language model into
schemas.
