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Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning
Xiaoming Shi · Siqiao Xue · Kangrui Wang · Fan Zhou · James Zhang · Jun Zhou · Chenhao Tan · Hongyuan Mei

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 accuracy of event sequence models. We design a modeling and prediction framework where a large 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 two challenging real-world datasets (Amazon Review and GDELT), we demonstrate that our framework---thanks to the reasoning ability of language models---could significantly outperform the state-of-the-art event sequence models.

Author Information

Xiaoming Shi (Antgroup)
Siqiao Xue (Ant Group)
Kangrui Wang (University of Chicago)
Fan Zhou (AntGroup)
James Zhang (Ant Group)
Jun Zhou (Ant Services Group)
Chenhao Tan (University of Chicago)
Hongyuan Mei (Toyota Technological Institute at Chicago)

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