ChatUIE：探索使用大语言模型的基于聊天的统一信息抽取
ChatUIE: Exploring Chat-based Unified Information Extraction using Large Language Models
AI总结：
针对大语言模型在信息抽取领域的能力局限，该研究提出基于ChatGLM的ChatUIE统一信息抽取框架，结合强化学习对齐任务与生成约束，实现了信息抽取性能的显著提升，仅伴随轻微的聊天能力下降。
摘要：
大语言模型的最新进展在通用聊天中展现了出色性能，但它们在特定领域的能力，尤其是信息抽取方面，存在一定局限。对于此前基于提示的方法而言，从偏离已知模式或指令的自然语言中抽取结构化信息已被证实颇具挑战。这促使我们探索聊天类语言模型的特定领域建模，以此作为从自然语言中抽取结构化信息的解决方案。本文提出ChatUIE，这是一个构建于ChatGLM之上的创新型统一信息抽取框架。同时，我们采用强化学习来优化并对齐涉及混淆样本与小样本的各类任务。此外，我们还集成了生成约束，以解决生成输入中不存在的元素的问题。实验结果表明，ChatUIE能够在聊天能力略有下降的情况下，显著提升信息抽取的性能。
英文摘要：
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.
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。
