用大模型打造真实911调度员训练系统，提升效率与公平性
Sim911: Towards Effective and Equitable 9-1-1 Dispatcher Training with an LLM-Enabled Simulation
- 基于真实通话数据生成仿真场景，还原真实应急对话
- 动态提示与向量库控制模型行为，确保训练目标一致
- 循环纠错验证机制，自动过滤低质输出提升质量
紧急救援服务对保障公共安全至关重要，9-1-1调度员作为一线人员，直接影响响应速度与应急效率。传统培训依赖经验人员角色扮演，耗时费力，且常忽视弱势群体需求。为此，我们提出Sim911——首个由大语言模型（LLM）驱动的9-1-1调度员训练仿真系统。其核心创新包括：(1) 知识构建：利用归档9-1-1通话数据生成贴近现实的模拟场景；(2) 上下文感知的受控生成：通过动态提示与向量基实现对LLM行为的精准调控；(3) 带循环修正的验证机制：自动筛选低质量响应并持续优化系统表现。该系统显著提升了培训的真实性、可扩展性与公平性。
原文摘要 · Abstract (English)
Emergency response services are vital for enhancing public safety by safeguarding the environment, property, and human lives. As frontline members of these services, 9-1-1 dispatchers have a direct impact on response times and the overall effectiveness of emergency operations. However, traditional dispatcher training methods, which rely on role-playing by experienced personnel, are labor-intensive, time-consuming, and often neglect the specific needs of underserved communities. To address these challenges, we introduce Sim911, the first training simulation for 9-1-1 dispatchers powered by Large Language Models (LLMs). Sim911 enhances training through three key technical innovations: (1) knowledge construction, which utilizes archived 9-1-1 call data to generate simulations that closely mirror real-world scenarios; (2) context-aware controlled generation, which employs dynamic prompts and vector bases to ensure that LLM behavior aligns with training objectives; and (3) validation with looped correction, which filters out low-quality responses and refines the system performance.
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