  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 method, but the existing data generation
methods either still rely on large-scale ground-truth data or cannot be applied
to complicated IE tasks due to their poor performance. To address these
challenges, we propose STAR, a data generation method that leverages Large
Language Models (LLMs) to synthesize data instances given limited seed
demonstrations, thereby boosting low-resource information extraction
performance. Our approach involves generating target structures (Y) followed by
generating passages (X), all accomplished with the aid of LLMs. We design
fine-grained step-by-step instructions to obtain the initial data instances. We
further reduce errors and improve data quality through self-reflection error
identification and self-refinement with iterative revision. Our experiments
show that the data generated by STAR significantly improve the performance of
low-resource event extraction and relation extraction tasks, even surpassing
the effectiveness of human-curated data. Human assessment of the data quality
shows STAR-generated data exhibits higher passage quality and better align with
the task definitions compared with the human-curated data.
