@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://aclanthology.org/2025.naacl-demo.3/",
doi = "10.18653/v1/2025.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{\$}."
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<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 CLEAR-Command, a system that leverages Large Language Models (LLMs) to enhance emergency communications. CLEAR stands for $textbfCoordinatedListening,Extraction, andAllocation inResponse. CLEAR-Command automates the transcription, summarization, and task extraction from live radio communications of emergency first responders using the OpenAI Whisper API for transcription and gpt-4o for summarization and task extraction. Our system provides a dynamic overview of task allocations and their execution status, significantly improving the accuracy of task identification and the clarity of communication. We evaluated our system through an expert pre-study with 4 experts and a user study with 13 participants. The expert pre-study identified gpt-4o as providing the most accurate task extraction, while the user study showed that CLEAR-Command significantly outperforms traditional radio communication in terms of clarity, trust, and correctness of task extraction. Our demo is hosted under thislink, and all project details are presented in ourGitlab page$.</abstract>
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%0 Conference Proceedings
%T CLEAR-Command: Coordinated Listening, Extraction, and Allocation for Emergency Response with Large Language Models
%A Doula, Achref
%A Bohlender, Bela
%A Mühlhäuser, Max
%A Sanchez Guinea, Alejandro
%Y Dziri, Nouha
%Y Ren, Sean (Xiang)
%Y Diao, Shizhe
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-191-9
%F doula-etal-2025-clear
%X 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 CLEAR-Command, a system that leverages Large Language Models (LLMs) to enhance emergency communications. CLEAR stands for $textbfCoordinatedListening,Extraction, andAllocation inResponse. CLEAR-Command automates the transcription, summarization, and task extraction from live radio communications of emergency first responders using the OpenAI Whisper API for transcription and gpt-4o for summarization and task extraction. Our system provides a dynamic overview of task allocations and their execution status, significantly improving the accuracy of task identification and the clarity of communication. We evaluated our system through an expert pre-study with 4 experts and a user study with 13 participants. The expert pre-study identified gpt-4o as providing the most accurate task extraction, while the user study showed that CLEAR-Command significantly outperforms traditional radio communication in terms of clarity, trust, and correctness of task extraction. Our demo is hosted under thislink, and all project details are presented in ourGitlab page$.
%R 10.18653/v1/2025.naacl-demo.3
%U https://aclanthology.org/2025.naacl-demo.3/
%U https://doi.org/10.18653/v1/2025.naacl-demo.3
%P 20-28
Markdown (Informal)
[CLEAR-Command: Coordinated Listening, Extraction, and Allocation for Emergency Response with Large Language Models](https://aclanthology.org/2025.naacl-demo.3/) (Doula et al., NAACL 2025)
ACL