  Event sequence models have been found to be highly effective in the analysis
and prediction of events. Building such models requires availability of
abundant high-quality event sequence data. In certain applications, however,
clean structured event sequences are not available, and automated sequence
extraction results in data that is too noisy and incomplete. In this work, we
explore the use of Large Language Models (LLMs) to generate event sequences
that can effectively be used for probabilistic event model construction. This
can be viewed as a mechanism of distilling event sequence knowledge from LLMs.
Our approach relies on a Knowledge Graph (KG) of event concepts with partial
causal relations to guide the generative language model for causal event
sequence generation. We show that our approach can generate high-quality event
sequences, filling a knowledge gap in the input KG. Furthermore, we explore how
the generated sequences can be leveraged to discover useful and more complex
structured knowledge from pattern mining and probabilistic event models. We
release our sequence generation code and evaluation framework, as well as
corpus of event sequence data.
