ChatUIE: Exploring Chat-based Unified Information Extraction using Large
  Language Models
Jun Xu Mengshu Sun Zhiqiang Zhang Jun Zhou
Recent advancements in large language models have shown impressive
performance in general chat. However, their domain-specific capabilities,
particularly in information extraction, have certain limitations. Extracting
structured information from natural language that deviates from known schemas
or instructions has proven challenging for previous prompt-based methods. This
motivated us to explore domain-specific modeling in chat-based language models
as a solution for extracting structured information from natural language. In
this paper, we present ChatUIE, an innovative unified information extraction
framework built upon ChatGLM. Simultaneously, reinforcement learning is
employed to improve and align various tasks that involve confusing and limited
samples. Furthermore, we integrate generation constraints to address the issue
of generating elements that are not present in the input. Our experimental
results demonstrate that ChatUIE can significantly improve the performance of
information extraction with a slight decrease in chatting ability.