STAR: Boosting Low-Resource Information Extraction by Structure-to-Text Data Generation with Large Language Models

arXiv:2305.15090v3 Announce Type: replace
Abstract: Information extraction tasks such as event extraction require an in-depth understanding of the output structure and sub-task dependencies. They heavily rely on task-specific training data in the form of (passage, target structure) pairs to obtain reasonable performance. However, obtaining such data through human annotation is costly, leading to a pressing need for low-resource information extraction approaches that require minimal human labeling for real-world applications. Fine-tuning supervised models with synthesized training data would be a generalizable …

abstract arxiv boosting cs.ai cs.cl data dependencies event extraction form information information extraction language language models large language large language models low performance star tasks task-specific training text through training training data type understanding