IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus

arXiv:2402.14710v1 Announce Type: cross
Abstract: 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 …

abstract arxiv capabilities cs.ai cs.cl cs.db cs.ir cs.lg current data datasets domains extraction gap information information extraction key language language models large language large language models llms performance quality scale schema small type vital