Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification
Sha Li, Ruining Zhao, Manling Li, Heng Ji, Chris Callison-Burch,, Jiawei Han

TL;DR
This paper introduces a novel method for inducing hierarchical event schemas from large language models using incremental prompting and verification, significantly improving the quality and complexity of generated schemas.
Contribution
It presents a new paradigm for event schema induction leveraging LLMs, with an incremental approach to handle complex graph structures more effectively.
Findings
7.2% F1 improvement in temporal relations
31.0% F1 improvement in hierarchical relations
Schemas rated higher in readability by human assessors
Abstract
Event schemas are a form of world knowledge about the typical progression of events. Recent methods for event schema induction use information extraction systems to construct a large number of event graph instances from documents, and then learn to generalize the schema from such instances. In contrast, we propose to treat event schemas as a form of commonsense knowledge that can be derived from large language models (LLMs). This new paradigm greatly simplifies the schema induction process and allows us to handle both hierarchical relations and temporal relations between events in a straightforward way. Since event schemas have complex graph structures, we design an incremental prompting and verification method to break down the construction of a complex event graph into three stages: event skeleton construction, event expansion, and event-event relation verification. Compared to…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopic Modeling · Advanced Text Analysis Techniques · Biomedical Text Mining and Ontologies
