IEPile：挖掘大规模基于模式的信息抽取语料库
IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus
AI总结：
针对信息抽取数据集规模小、碎片化且缺乏标准模式的问题，本文构建了包含约0.32B词元的双语指令语料库IEPile，通过基于模式的指令生成方法，显著提升了LLMs在信息抽取上的性能，尤其在零样本泛化方面。
摘要：
大型语言模型（LLMs）在各领域展现出显著潜力，但在信息抽取（IE）方面存在明显的性能差距。值得注意的是，高质量的指令数据是提升LLMs特定能力的关键，而当前的信息抽取数据集往往规模小、碎片化且缺乏标准化的模式。为此，我们引入了IEPile，一个全面的双语（英文和中文）信息抽取指令语料库，包含约0.32B个词元。我们通过收集和清理33个现有信息抽取数据集来构建IEPile，并引入基于模式的指令生成以挖掘大规模语料库。实验表明，IEPile提升了LLMs在信息抽取上的性能，尤其在零样本泛化方面有显著改进。我们开源了资源和预训练模型，希望为NLP社区提供有价值的支持。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
