Instruction-Tuning LLMs for Event Extraction with Annotation Guidelines
This addresses event extraction in NLP, offering incremental improvements for schema adaptation and rare event handling.
The researchers investigated how annotation guidelines affect instruction-tuning of large language models for event extraction, finding that guidelines improve cross-schema generalization and low-frequency event-type performance when sufficient training data is available.
In this work, we study the effect of annotation guidelines -- textual descriptions of event types and arguments, when instruction-tuning large language models for event extraction. We conducted a series of experiments with both human-provided and machine-generated guidelines in both full- and low-data settings. Our results demonstrate the promise of annotation guidelines when there is a decent amount of training data and highlight its effectiveness in improving cross-schema generalization and low-frequency event-type performance.