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

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Abstract

Emergencies, disasters, and other forms of 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 paper briefly explores two potential use cases: supporting 911 dispatch and providing social media informed personalized protective action guidance to the public. 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.