mlsanityData to 7 Oct 2026

LLM-Assisted Crisis Management: Building Advanced LLM Platforms for Effective Emergency Response and Public Collaboration

arXiv 2402.10908 ↗First posted 12 Jan 2024cs.CL cs.AI cs.HCcited by 20

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Abstract (arXiv)

Emergencies and critical incidents often unfold rapidly, necessitating a swift and effective response. In this research, we introduce a novel approach to identify and classify emergency situations from social media posts and direct emergency messages using an open source Large Language Model, LLAMA2. The goal is to harness the power of natural language processing and machine learning to assist public safety telecommunicators and huge crowds during countrywide emergencies. Our research focuses on developing a language model that can understand users describe their situation in the 911 call, enabling LLAMA2 to analyze the content and offer relevant instructions to the telecommunicator, while also creating workflows to notify government agencies with the caller's information when necessary. Another benefit this language model provides is its ability to assist people during a significant emergency incident when the 911 system is overwhelmed, by assisting the users with simple instructions and informing authorities with their location and emergency information.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 7 of its 51 references that are in the map. First posted on arXiv 12 Jan 2024.

Builds on

7 of 51 references.

References · 7

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2023FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioningcited by 2,551
  3. 2018Manipulating and Measuring Model Interpretabilitycited by 256
  4. 2023Auditing large language models: a three-layered approachcited by 135
  5. 2019"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanationscited by 111
  6. 2023NEFTune: Noisy Embeddings Improve Instruction Finetuningcited by 99
  7. 2022Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoningcited by 93

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 11; 2026: 5.

20 papers.

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  1. 2025Harnessing Large Language Models for Disaster Management: A Surveycited by 20
  2. 2025A Survey of Scaling in Large Language Model Reasoningcited by 10
  3. 2024DisasterQA: A Benchmark for Assessing the performance of LLMs in Disaster Responsecited by 8
  4. 2024Evaluating Robustness of LLMs on Crisis-Related Microblogs across Events, Information Types, and Linguistic Featurescited by 7
  5. 2025Towards Evaluating Proactive Risk Awareness of Multimodal Language Modelscited by 6
  6. 2025LLMs as World Models: Data-Driven and Human-Centered Pre-Event Simulation for Disaster Impact Assessmentcited by 6
  7. 2025Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applicationscited by 4
  8. 2024Gen-AI for User Safety: A Surveycited by 2
  9. 2025Urban Computing in the Era of Large Language Modelscited by 2
  10. 2025EdgeProfiler: A Fast Profiling Framework for Lightweight LLMs on Edge Using Analytical Modelcited by 2
  11. 2025DispatchMAS: Fusing taxonomy and artificial intelligence agents for emergency medical servicescited by 2
  12. 2025Semantic Scheduling for LLM Inferencecited by 1
8 more
  1. 2024Efficient VoIP Communications through LLM-based Real-Time Speech Reconstruction and Call Prioritization for Emergency Servicescited by 0
  2. 2025ClimaEmpact: Domain-Aligned Small Language Models and Datasets for Extreme Weather Analyticscited by 0
  3. 2025A Dynamic Fusion Model for Consistent Crisis Responsecited by 0
  4. 2026Digital Twin and Agentic AI for Wild Fire Disaster Management: Intelligent Virtual Situation Roomcited by 0
  5. 2026A Lightweight LLM Framework for Disaster Humanitarian Information Classificationcited by 0
  6. 2026LLM-Powered Automatic Translation and Urgency in Crisis Scenarioscited by 0
  7. 2026Guide Me Out: A Framework to Benchmark VLM Operators Communication in Crisis Scenarioscited by 0
  8. 2026From Sports to Safety: Benchmarking Proactive Risk Inference in MLLMscited by 0