@inproceedings{lei-etal-2025-harnessing,
    title = "Harnessing Large Language Models for Disaster Management: A Survey",
    author = "Lei, Zhenyu  and
      Dong, Yushun  and
      Li, Weiyu  and
      Ding, Rong  and
      Wang, Qi R.  and
      Li, Jundong",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.findings-acl.750/",
    doi = "10.18653/v1/2025.findings-acl.750",
    pages = "14528--14551",
    ISBN = "979-8-89176-256-5",
    abstract = "Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, including their emerging role in mitigating threats to human life, infrastructure, and the environment during natural disasters. Despite increasing research on disaster-focused LLMs, there remains a lack of systematic reviews and in-depth analyses of their applications in natural disaster management. To address this gap, this paper presents a comprehensive survey of LLMs in disaster response, introducing a taxonomy that categorizes existing works based on disaster phases and application scenarios. By compiling public datasets and identifying key challenges and opportunities, this study aims to provide valuable insights for the research community and practitioners in developing advanced LLM-driven solutions to enhance resilience against natural disasters."
}