@inproceedings{zhu-etal-2026-large,
title = "Can Large Language Models Effectively Support Decision-Making in Sudden Emergencies?",
author = "Zhu, Mengna and
Wu, Jibing and
Liu, Lihua and
Gong, Yuran and
Hao, Yang and
Yachao, Fu and
Wang, Mao and
Hou, Lei and
Li, Juanzi",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1820/",
pages = "36536--36558",
ISBN = "979-8-89176-395-1",
abstract = "Emergency response is a safety-critical public governance task that demands accurate and timely decision-making based on complex event information. This process involves multiple stages, including information collection, integration, analysis, risk assessment, and decision recommendation. Existing research has predominantly concentrated on the earlier stages, while studies focusing on the decision support phase remain underexplored, primarily due to the lack of suitable datasets for reliable and compliance-aware decision-oriented modeling and evaluation. To bridge this gap, we introduce the first real-world Emergency Decision-Making dataset EDM-Bench, comprising 1,179 instances spanning diverse task formats, including judgment, choice, short-answer, and structured emergency report generation. We also construct a structured rule repository, EDM-R{\texttwosuperior}, which contains 3,406 parsed emergency regulations to enhance decision reliability. Building on these resources, we propose a rule-enhanced reasoning framework, R{\textthreesuperior}V-EDM, which integrates external regulatory knowledge with constrained inference mechanisms to improve both decision safety and interpretability. Extensive experiments demonstrate the inherent complexity of emergency decision-making and validate the effectiveness of our approach in enabling more reliable and trustworthy decisions."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhu-etal-2026-large">
<titleInfo>
<title>Can Large Language Models Effectively Support Decision-Making in Sudden Emergencies?</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mengna</namePart>
<namePart type="family">Zhu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jibing</namePart>
<namePart type="family">Wu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lihua</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yuran</namePart>
<namePart type="family">Gong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yang</namePart>
<namePart type="family">Hao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Fu</namePart>
<namePart type="family">Yachao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mao</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lei</namePart>
<namePart type="family">Hou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Juanzi</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Liakata</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Viviane</namePart>
<namePart type="given">P</namePart>
<namePart type="family">Moreira</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiajun</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Jurgens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, United States</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-395-1</identifier>
</relatedItem>
<abstract>Emergency response is a safety-critical public governance task that demands accurate and timely decision-making based on complex event information. This process involves multiple stages, including information collection, integration, analysis, risk assessment, and decision recommendation. Existing research has predominantly concentrated on the earlier stages, while studies focusing on the decision support phase remain underexplored, primarily due to the lack of suitable datasets for reliable and compliance-aware decision-oriented modeling and evaluation. To bridge this gap, we introduce the first real-world Emergency Decision-Making dataset EDM-Bench, comprising 1,179 instances spanning diverse task formats, including judgment, choice, short-answer, and structured emergency report generation. We also construct a structured rule repository, EDM-R², which contains 3,406 parsed emergency regulations to enhance decision reliability. Building on these resources, we propose a rule-enhanced reasoning framework, R³V-EDM, which integrates external regulatory knowledge with constrained inference mechanisms to improve both decision safety and interpretability. Extensive experiments demonstrate the inherent complexity of emergency decision-making and validate the effectiveness of our approach in enabling more reliable and trustworthy decisions.</abstract>
<identifier type="citekey">zhu-etal-2026-large</identifier>
<location>
<url>https://aclanthology.org/2026.findings-acl.1820/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>36536</start>
<end>36558</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Can Large Language Models Effectively Support Decision-Making in Sudden Emergencies?
%A Zhu, Mengna
%A Wu, Jibing
%A Liu, Lihua
%A Gong, Yuran
%A Hao, Yang
%A Yachao, Fu
%A Wang, Mao
%A Hou, Lei
%A Li, Juanzi
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhu-etal-2026-large
%X Emergency response is a safety-critical public governance task that demands accurate and timely decision-making based on complex event information. This process involves multiple stages, including information collection, integration, analysis, risk assessment, and decision recommendation. Existing research has predominantly concentrated on the earlier stages, while studies focusing on the decision support phase remain underexplored, primarily due to the lack of suitable datasets for reliable and compliance-aware decision-oriented modeling and evaluation. To bridge this gap, we introduce the first real-world Emergency Decision-Making dataset EDM-Bench, comprising 1,179 instances spanning diverse task formats, including judgment, choice, short-answer, and structured emergency report generation. We also construct a structured rule repository, EDM-R², which contains 3,406 parsed emergency regulations to enhance decision reliability. Building on these resources, we propose a rule-enhanced reasoning framework, R³V-EDM, which integrates external regulatory knowledge with constrained inference mechanisms to improve both decision safety and interpretability. Extensive experiments demonstrate the inherent complexity of emergency decision-making and validate the effectiveness of our approach in enabling more reliable and trustworthy decisions.
%U https://aclanthology.org/2026.findings-acl.1820/
%P 36536-36558
Markdown (Informal)
[Can Large Language Models Effectively Support Decision-Making in Sudden Emergencies?](https://aclanthology.org/2026.findings-acl.1820/) (Zhu et al., Findings 2026)
ACL
- Mengna Zhu, Jibing Wu, Lihua Liu, Yuran Gong, Yang Hao, Fu Yachao, Mao Wang, Lei Hou, and Juanzi Li. 2026. Can Large Language Models Effectively Support Decision-Making in Sudden Emergencies?. In Findings of the Association for Computational Linguistics: ACL 2026, pages 36536–36558, San Diego, California, United States. Association for Computational Linguistics.