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

arXiv: https://arxiv.org/abs/2402.10908 · HTML: https://mlsanity.com/paper/2402.10908/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 51 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Builds on (most cited first, 7 of 7)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018)
- [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023)
- ["How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations](https://mlsanity.com/paper/1911.06473/) (2019)
- [NEFTune: Noisy Embeddings Improve Instruction Finetuning](https://mlsanity.com/paper/2310.05914/) (2023)
- [Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning](https://mlsanity.com/paper/2205.01992/) (2022)

## Built on by (most cited first, 21 of 21)

- [Harnessing Large Language Models for Disaster Management: A Survey](https://mlsanity.com/paper/2501.06932/) (2025)
- [A Survey of Scaling in Large Language Model Reasoning](https://mlsanity.com/paper/2504.02181/) (2025)
- [DisasterQA: A Benchmark for Assessing the performance of LLMs in Disaster Response](https://mlsanity.com/paper/2410.20707/) (2024)
- [Evaluating Robustness of LLMs on Crisis-Related Microblogs across Events, Information Types, and Linguistic Features](https://mlsanity.com/paper/2412.10413/) (2024)
- [Towards Evaluating Proactive Risk Awareness of Multimodal Language Models](https://mlsanity.com/paper/2505.17455/) (2025)
- [LLMs as World Models: Data-Driven and Human-Centered Pre-Event Simulation for Disaster Impact Assessment](https://mlsanity.com/paper/2506.06355/) (2025)
- [Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications](https://mlsanity.com/paper/2507.00914/) (2025)
- [Gen-AI for User Safety: A Survey](https://mlsanity.com/paper/2411.06606/) (2024)
- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)
- [EdgeProfiler: A Fast Profiling Framework for Lightweight LLMs on Edge Using Analytical Model](https://mlsanity.com/paper/2506.09061/) (2025)
- [DispatchMAS: Fusing taxonomy and artificial intelligence agents for emergency medical services](https://mlsanity.com/paper/2510.21228/) (2025)
- [Semantic Scheduling for LLM Inference](https://mlsanity.com/paper/2506.12204/) (2025)
- [LLM-Powered Automatic Translation and Urgency in Crisis Scenarios](https://mlsanity.com/paper/2602.13452/) (2026)
- [Efficient VoIP Communications through LLM-based Real-Time Speech Reconstruction and Call Prioritization for Emergency Services](https://mlsanity.com/paper/2412.16176/) (2024)
- [ClimaEmpact: Domain-Aligned Small Language Models and Datasets for Extreme Weather Analytics](https://mlsanity.com/paper/2504.19066/) (2025)
- [A Dynamic Fusion Model for Consistent Crisis Response](https://mlsanity.com/paper/2509.01053/) (2025)
- [Digital Twin and Agentic AI for Wild Fire Disaster Management: Intelligent Virtual Situation Room](https://mlsanity.com/paper/2602.08949/) (2026)
- [A Lightweight LLM Framework for Disaster Humanitarian Information Classification](https://mlsanity.com/paper/2602.12284/) (2026)
- [Guide Me Out: A Framework to Benchmark VLM Operators Communication in Crisis Scenarios](https://mlsanity.com/paper/2606.09428/) (2026)
- [From Sports to Safety: Benchmarking Proactive Risk Inference in MLLMs](https://mlsanity.com/paper/2608.05560/) (2026)
- [EmoRSS: Mitigating Emotion-Induced Over-Refusal in Large Language Models](https://mlsanity.com/paper/2610.04998/) (2026)

## 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.