  Large Language Models (LLMs) demonstrate remarkable potential across various
domains; however, they exhibit a significant performance gap in Information
Extraction (IE). Note that high-quality instruction data is the vital key for
enhancing the specific capabilities of LLMs, while current IE datasets tend to
be small in scale, fragmented, and lack standardized schema. To this end, we
introduce IEPile, a comprehensive bilingual (English and Chinese) IE
instruction corpus, which contains approximately 0.32B tokens. We construct
IEPile by collecting and cleaning 33 existing IE datasets, and introduce
schema-based instruction generation to unearth a large-scale corpus.
Experimentally, IEPile enhance the performance of LLMs for IE, with notable
improvements in zero-shot generalization. We open-source the resource and
pre-trained models, hoping to provide valuable support to the NLP community.
