The difficulty of the information extraction task lies in dealing with the
task-specific label schemas and heterogeneous data structures. Recent work has
proposed methods based on large language models to uniformly model different
information extraction tasks. However, these existing methods are deficient in
their information extraction capabilities for Chinese languages other than
English. In this paper, we propose an end-to-end chat-enhanced instruction
tuning framework for universal information extraction (YAYI-UIE), which
supports both Chinese and English. Specifically, we utilize dialogue data and
information extraction data to enhance the information extraction performance
jointly. Experimental results show that our proposed framework achieves
state-of-the-art performance on Chinese datasets while also achieving
comparable performance on English datasets under both supervised settings and
zero-shot settings.

我们在这篇论文中提出了一种面向普适信息提取的端到端聊天增强指导调优框架（YAYI-UIE），旨在提高中英文信息提取性能，实验结果表明，我们的框架在中文数据集上取得了最先进的性能，同时在英文数据集上在监督和零样本设置下也获得了可比的性能。