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.
Experimental results on LLaMA and Baichuan demonstrate that using IEPile can
enhance the performance of LLMs for IE, especially the zero-shot
generalization. We open-source the resource and pre-trained models, hoping to
provide valuable support to the NLP community.

通过构建综合的双语（英语和汉语）信息抽取指示语料库 IEPile，可以提高大语言模型在信息抽取中的性能，尤其是零样本泛化能力。