分解、丰富与提取！基于 LLMs 的模式感知事件抽取
Decompose, Enrich, and Extract! Schema-aware Event Extraction using LLMs
AI总结：
本文提出一种基于 LLMs 的自动化事件抽取新方法，通过将任务分解为事件检测与论元抽取以缓解幻觉，并整合动态模式感知的检索增强提示技术，在多个基准上表现优于基线。
摘要：
大型语言模型（LLMs）在处理自然语言数据方面展现出卓越能力，有望从多样化文本源中高效提取知识以增强态势感知并支持决策。然而，其易产生幻觉从而导致上下文不准确内容的担忧随之而来。本工作致力于利用 LLMs 进行自动化事件抽取，通过将任务分解为事件检测和事件论元抽取来应对幻觉问题。此外，该方法将动态模式感知的增强检索示例整合到为每个特定查询量身定制的提示中，从而扩展并适应了检索增强生成等高级提示技术。在知名事件抽取基准上的评估结果及合成基准的结果表明，该方法优于基线方法。
英文摘要：
Large Language Models (LLMs) demonstrate significant capabilities in processing natural language data, promising efficient knowledge extraction from diverse textual sources to enhance situational awareness and support decision-making. However, concerns arise due to their susceptibility to hallucination, resulting in contextually inaccurate content. This work focuses on harnessing LLMs for automated Event Extraction, introducing a new method to address hallucination by decomposing the task into Event Detection and Event Argument Extraction. Moreover, the proposed method integrates dynamic schema-aware augmented retrieval examples into prompts tailored for each specific inquiry, thereby extending and adapting advanced prompting techniques such as Retrieval-Augmented Generation. Evaluation findings on prominent event extraction benchmarks and results from a synthesized benchmark illustrate the method's superior performance compared to baseline approaches.
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。
