@inproceedings{doula-etal-2025-clear,
    title = "{CLEAR}-Command: Coordinated Listening, Extraction, and Allocation for Emergency Response with Large Language Models",
    author = {Doula, Achref  and
      Bohlender, Bela  and
      M{\"u}hlh{\"a}user, Max  and
      Sanchez Guinea, Alejandro},
    editor = "Dziri, Nouha  and
      Ren, Sean (Xiang)  and
      Diao, Shizhe",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.naacl-demo.3/",
    pages = "20--28",
    ISBN = "979-8-89176-191-9",
    abstract = "Effective communication is vital in emergency response scenarios where clarity and speed can save lives. Traditional systems often struggle under the chaotic conditions of real-world emergencies, leading to breakdowns in communication and task management. This paper introduces $\textbf{CLEAR}$-Command, a system that leverages Large Language Models (LLMs) to enhance emergency communications. $\textbf{CLEAR}$ stands for {\$}textbfC$oordinated$\textbf{L}$istening,$\textbf{E}$xtraction, and$\textbf{A}$llocation in$\textbf{R}$esponse. CLEAR-Command automates the transcription, summarization, and task extraction from live radio communications of emergency first responders using the OpenAI Whisper API for transcription and gpt-4o for summarization and task extraction. Our system provides a dynamic overview of task allocations and their execution status, significantly improving the accuracy of task identification and the clarity of communication. We evaluated our system through an expert pre-study with 4 experts and a user study with 13 participants. The expert pre-study identified gpt-4o as providing the most accurate task extraction, while the user study showed that CLEAR-Command significantly outperforms traditional radio communication in terms of clarity, trust, and correctness of task extraction. Our demo is hosted under this$link$, and all project details are presented in our$Gitlab page{\$}."
}