  Large language models have shown astonishing performance on a wide range of
reasoning tasks. In this paper, we investigate whether they could reason about
real-world events and help improve the prediction performance of event sequence
models. We design LAMP, a framework that integrates a large language model in
event prediction. Particularly, the language model performs abductive reasoning
to assist an event sequence model: the event model proposes predictions on
future events given the past; instructed by a few expert-annotated
demonstrations, the language model learns to suggest possible causes for each
proposal; a search module finds out the previous events that match the causes;
a scoring function learns to examine whether the retrieved events could
actually cause the proposal. Through extensive experiments on several
challenging real-world datasets, we demonstrate that our framework -- thanks to
the reasoning capabilities of large language models -- could significantly
outperform the state-of-the-art event sequence models.
