Instruction-Tuning LLMs for Event Extraction with Annotation Guidelines

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. 1* The first two authors contribute equally. 1 Our source code and datasets are available at https://github.com/Ziyu-Yao-NLP-Lab/PyCode-TextEE.# This is an event extraction task ... # The following lines describe the task definition @dataclass class Extradite(JusticeEvent): mention