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.

翻译：近期大语言模型的进展在通用对话中展现出显著性能，但其在信息抽取等特定领域的能力仍存在局限。现有基于提示的方法在从偏离已知模式或指令的自然语言中提取结构化信息时面临挑战，这促使我们探索基于聊天语言模型的领域专用建模方案，以实现自然语言的结构化信息抽取。本文提出ChatUIE——一种基于ChatGLM的创新统一信息抽取框架。该框架同时采用强化学习来优化和协调涉及混淆样本与有限样本的多类任务，并通过集成生成约束解决输入中缺失元素的生成问题。实验结果表明，ChatUIE能在聊天能力略有下降的情况下显著提升信息抽取性能。