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.

通过使用开放源代码的大型语言模型 LLAMA2，我们介绍了一种新的方法来从社交媒体帖子和直接应急信息中识别和分类紧急情况，旨在利用自然语言处理和机器学习的力量，为公共安全通信员和大规模人群在全国范围内的紧急情况提供帮助，并开发了一种语言模型，使其能够理解用户在 911 呼叫中描述的情况，使 LLAMA2 能够分析内容并向通信员提供相关指示，并在必要时创建工作流程以通知政府机构与呼叫者信息。这种语言模型的另一个好处是在 911 系统不堪重负时能够帮助用户提供简单的指导并向相关部门提供其位置和紧急信息。