We propose a multi-agent debate as optimization (DAO) system for event
extraction, where the primary objective is to iteratively refine the large
language models (LLMs) outputs through debating without parameter tuning. In
DAO, we introduce two novel modules: the Diverse-RAG (DRAG) module and the
Adaptive Conformal Prediction (AdaCP) module. DRAG systematically retrieves
supporting information that best fits the debate discussion, while AdaCP
enhances the accuracy and reliability of event extraction by effectively
rejecting less promising answers. Experimental results demonstrate a
significant reduction in the performance gap between supervised approaches and
tuning-free LLM-based methods by 18.1% and 17.8% on ACE05 and 17.9% and 15.2%
on CASIE for event detection and argument extraction respectively.

我们提出了一个多智能体辩论作为优化的事件提取系统，通过辩论而无需参数调整来迭代改进大型语言模型的输出。在该系统中，我们引入了两个新模块：Diverse-RAG 模块和 Adaptive Conformal Prediction 模块。实验结果表明，相对于有监督方法和无需调参的基于大型语言模型的方法，在 ACE05 和 CASIE 事件检测以及论证提取方面，性能差距分别降低了 18.1％和 17.8％以及 17.9％和 15.2％。