LLM-Assisted Crisis Management: Building Advanced LLM Platforms for Effective Emergency Response and Public Collaboration
This addresses the challenge of overwhelmed emergency systems during crises for public safety agencies and affected populations, though it appears incremental as it applies an existing model to a new domain.
The researchers tackled the problem of rapid emergency response by developing a system using the LLAMA2 large language model to identify and classify emergency situations from social media posts and direct messages, aiming to assist public safety telecommunicators and crowds during countrywide emergencies by analyzing 911 calls and providing instructions.
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.