  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.
