@inproceedings{yin-etal-2025-disastir,
title = "{D}isast{IR}: A Comprehensive Information Retrieval Benchmark for Disaster Management",
author = "Yin, Kai and
Dong, Xiangjue and
Liu, Chengkai and
Huang, Lipai and
Xiao, Yiming and
Liu, Zhewei and
Mostafavi, Ali and
Caverlee, James",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.97/",
doi = "10.18653/v1/2025.findings-emnlp.97",
pages = "1836--1867",
ISBN = "979-8-89176-335-7",
abstract = "Effective disaster management requires timely access to accurate and contextually relevant information. Existing Information Retrieval (IR) benchmarks, however, focus primarily on general or specialized domains, such as medicine or finance, neglecting the unique linguistic complexity and diverse information needs encountered in disaster management scenarios. To bridge this gap, we introduce DisastIR, the first comprehensive IR evaluation benchmark specifically tailored for disaster management. DisastIR comprises 9,600 diverse user queries and more than 1.3 million labeled query-passage pairs, covering 48 distinct retrieval tasks derived from six search intents and eight general disaster categories that include 301 specific event types. Our evaluations of 30 state-of-the-art retrieval models demonstrate significant performance variances across tasks, with no single model excelling universally. Furthermore, comparative analyses reveal significant performance gaps between general-domain and disaster management-specific tasks, highlighting the necessity of disaster management-specific benchmarks for guiding IR model selection to support effective decision-making in disaster management scenarios. All source codes and DisastIR are available at https://github.com/KaiYin97/Disaster{\_}IR."
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<abstract>Effective disaster management requires timely access to accurate and contextually relevant information. Existing Information Retrieval (IR) benchmarks, however, focus primarily on general or specialized domains, such as medicine or finance, neglecting the unique linguistic complexity and diverse information needs encountered in disaster management scenarios. To bridge this gap, we introduce DisastIR, the first comprehensive IR evaluation benchmark specifically tailored for disaster management. DisastIR comprises 9,600 diverse user queries and more than 1.3 million labeled query-passage pairs, covering 48 distinct retrieval tasks derived from six search intents and eight general disaster categories that include 301 specific event types. Our evaluations of 30 state-of-the-art retrieval models demonstrate significant performance variances across tasks, with no single model excelling universally. Furthermore, comparative analyses reveal significant performance gaps between general-domain and disaster management-specific tasks, highlighting the necessity of disaster management-specific benchmarks for guiding IR model selection to support effective decision-making in disaster management scenarios. All source codes and DisastIR are available at https://github.com/KaiYin97/Disaster_IR.</abstract>
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%0 Conference Proceedings
%T DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Management
%A Yin, Kai
%A Dong, Xiangjue
%A Liu, Chengkai
%A Huang, Lipai
%A Xiao, Yiming
%A Liu, Zhewei
%A Mostafavi, Ali
%A Caverlee, James
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F yin-etal-2025-disastir
%X Effective disaster management requires timely access to accurate and contextually relevant information. Existing Information Retrieval (IR) benchmarks, however, focus primarily on general or specialized domains, such as medicine or finance, neglecting the unique linguistic complexity and diverse information needs encountered in disaster management scenarios. To bridge this gap, we introduce DisastIR, the first comprehensive IR evaluation benchmark specifically tailored for disaster management. DisastIR comprises 9,600 diverse user queries and more than 1.3 million labeled query-passage pairs, covering 48 distinct retrieval tasks derived from six search intents and eight general disaster categories that include 301 specific event types. Our evaluations of 30 state-of-the-art retrieval models demonstrate significant performance variances across tasks, with no single model excelling universally. Furthermore, comparative analyses reveal significant performance gaps between general-domain and disaster management-specific tasks, highlighting the necessity of disaster management-specific benchmarks for guiding IR model selection to support effective decision-making in disaster management scenarios. All source codes and DisastIR are available at https://github.com/KaiYin97/Disaster_IR.
%R 10.18653/v1/2025.findings-emnlp.97
%U https://aclanthology.org/2025.findings-emnlp.97/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.97
%P 1836-1867
Markdown (Informal)
[DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Management](https://aclanthology.org/2025.findings-emnlp.97/) (Yin et al., Findings 2025)
ACL
- Kai Yin, Xiangjue Dong, Chengkai Liu, Lipai Huang, Yiming Xiao, Zhewei Liu, Ali Mostafavi, and James Caverlee. 2025. DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Management. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 1836–1867, Suzhou, China. Association for Computational Linguistics.