Emergency management urgently requires comprehensive knowledge while having a high possibility to go beyond individuals' cognitive scope. Therefore, artificial intelligence(AI) supported decision-making under that circumstance is of vital importance. Recent emerging large language models (LLM) provide a new direction for enhancing targeted machine intelligence. However, the utilization of LLM directly would inevitably introduce unreliable output for its inherent issue of hallucination and poor reasoning skills. In this work, we develop a system called Enhancing Emergency decision-making with Knowledge Graph and LLM (E-KELL), which provides evidence-based decision-making in various emergency stages. The study constructs a structured emergency knowledge graph and guides LLMs to reason over it via a prompt chain. In real-world evaluations, E-KELL receives scores of 9.06, 9.09, 9.03, and 9.09 in comprehensibility, accuracy, conciseness, and instructiveness from a group of emergency commanders and firefighters, demonstrating a significant improvement across various situations compared to baseline models. This work introduces a novel approach to providing reliable emergency decision support.

翻译：应急管理迫切需要全面知识，且极易超出个人认知范围。因此，在该情境下由人工智能（AI）支持的决策至关重要。近期兴起的大语言模型（LLM）为增强针对性的机器智能提供了新方向。然而，直接使用LLM会因其固有的幻觉问题和推理能力薄弱而不可避免地产生不可靠输出。本研究开发了一个名为“基于知识图谱和大语言模型的应急决策增强”（E-KELL）的系统，可在不同应急阶段提供基于证据的决策。该研究构建了结构化的应急知识图谱，并通过提示链引导LLM对其进行推理。在实际评估中，E-KELL在可理解性、准确性、简洁性和指导性方面分别获得应急指挥官和消防员群体9.06、9.09、9.03和9.09的评分，与基线模型相比，在各种情境下均展现出显著提升。本研究为提供可靠的应急决策支持引入了一种新方法。