LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration

📄 arXiv: 2505.03985v1 📥 PDF

作者: Zirong Chen, Ziyan An, Jennifer Reynolds, Kristin Mullen, Stephen Martini, Meiyi Ma

分类: cs.AI, cs.SE

发布日期: 2025-05-06

备注: Accepted at IJCAI-2025

💡 一句话要点

提出LogiDebrief以解决911呼叫评估效率低下问题

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 信号时序逻辑 大型语言模型 自动化评估 911呼叫 紧急响应服务 质量保证 人工智能

📋 核心要点

  1. 现有的人工评估方法在高呼叫量下难以保证评估的及时性和覆盖率，影响了911接警员的表现评估。
  2. LogiDebrief通过将接警要求形式化为逻辑规范，结合STL与LLM，实现了911呼叫的自动化评估。
  3. 在实际应用中，LogiDebrief成功处理了1701个真实呼叫，显著提高了评估效率，节省了大量人工时间。

📝 摘要（中文）

紧急响应服务对公共安全至关重要，911接警员在确保及时有效的紧急操作中发挥着关键作用。为了确保接警员的表现一致性，传统的人工评估面临高呼叫量的挑战，导致覆盖率低和评估延迟。本文提出LogiDebrief，一个基于信号时序逻辑（STL）与大型语言模型（LLM）集成的AI驱动框架，自动化911呼叫的评估。LogiDebrief将接警要求形式化为逻辑规范，通过三步验证过程进行系统评估，已在实际应用中证明其有效性，成功处理1701个真实呼叫，节省了311.85小时的人工参与。

🔬 方法详解

问题定义：本文旨在解决传统911呼叫评估方法在高呼叫量下的低覆盖率和延迟问题。现有的人工评估方式难以满足快速和全面的评估需求。

核心思路：LogiDebrief通过将911呼叫的评估过程自动化，利用信号时序逻辑（STL）和大型语言模型（LLM）进行系统性评估，确保评估的准确性和一致性。

技术框架：LogiDebrief的整体架构包括三个主要模块：第一步是上下文理解，识别响应者类型、事件分类和关键条件；第二步是基于STL的运行时检查，结合LLM确保合规性；第三步是结果的自动汇总，形成质量保证报告。

关键创新：LogiDebrief的创新在于将STL与LLM结合，形成了一种新的自动化评估框架，显著提高了911呼叫评估的效率和准确性，与传统方法相比具有本质的区别。

关键设计：在设计中，关键参数包括STL规范的定义和LLM的集成方式，确保了系统在处理复杂呼叫时的灵活性和准确性。

📊 实验亮点

LogiDebrief在实际应用中成功处理了1701个911呼叫，节省了311.85小时的人工参与。通过与传统评估方法的对比，验证了其在准确性和效率上的显著提升，展示了强大的实用价值。

🎯 应用场景

LogiDebrief的潜在应用领域包括紧急响应服务、公共安全评估和智能客服系统等。其自动化评估能力能够显著提高应急服务的响应效率和质量，未来可能在更多领域推广应用，提升整体服务水平。

📄 摘要（原文）

Emergency response services are critical to public safety, with 9-1-1 call-takers playing a key role in ensuring timely and effective emergency operations. To ensure call-taking performance consistency, quality assurance is implemented to evaluate and refine call-takers' skillsets. However, traditional human-led evaluations struggle with high call volumes, leading to low coverage and delayed assessments. We introduce LogiDebrief, an AI-driven framework that automates traditional 9-1-1 call debriefing by integrating Signal-Temporal Logic (STL) with Large Language Models (LLMs) for fully-covered rigorous performance evaluation. LogiDebrief formalizes call-taking requirements as logical specifications, enabling systematic assessment of 9-1-1 calls against procedural guidelines. It employs a three-step verification process: (1) contextual understanding to identify responder types, incident classifications, and critical conditions; (2) STL-based runtime checking with LLM integration to ensure compliance; and (3) automated aggregation of results into quality assurance reports. Beyond its technical contributions, LogiDebrief has demonstrated real-world impact. Successfully deployed at Metro Nashville Department of Emergency Communications, it has assisted in debriefing 1,701 real-world calls, saving 311.85 hours of active engagement. Empirical evaluation with real-world data confirms its accuracy, while a case study and extensive user study highlight its effectiveness in enhancing call-taking performance.