LLM-Assisted Crisis Management: Building Advanced LLM Platforms for Effective Emergency Response and Public Collaboration (2402.10908v1)

Abstract: 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 LLM, 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 LLM 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 LLM 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.

• The paper integrates LLMs into 911 dispatch to improve emergency call accuracy and multilingual processing efficiency.
• It introduces an LLM-enhanced mobile app that provides real-time guidance and categorizes emergencies using advanced analytics.
• The study highlights trade-offs between model size and performance, while addressing ethical considerations in deploying LLMs for crisis management.

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

Introduction

The paper "LLM-Assisted Crisis Management: Building Advanced LLM Platforms for Effective Emergency Response and Public Collaboration" addresses the challenges in emergency response systems, particularly in urban settings characterized by linguistic diversity and a high incidence of crises. Traditional systems often suffer from information overload and communication bottlenecks. This paper explores the integration of LLMs, specifically LLAMA2 and Mistral, to enhance 911 dispatch efficiency and public collaboration during emergencies.

Enhancing 911 Dispatch Efficiency

The central innovation of this framework is the integration of LLMs into the 911 dispatch process. An LLM works alongside human dispatchers to process emergency calls, enhancing the accuracy and efficiency of information transmission. This system includes real-time transcription, text segmentation, and named entity recognition (NER) to handle multilingual inputs effectively. The accuracy of incident classification is improved, facilitating better resource allocation and response coordination. This LLM integration also aids in overcoming language barriers, assisting dispatchers in diverse urban environments like New York City.

Public Collaboration and Crisis Management

The second framework focuses on public collaboration. It involves an LLM-enhanced mobile app that provides real-time, AI-driven instructions to the public during major emergencies. Managed by emergency response centers, this app guides users on safe shelter locations and supplies, using real-time analytics to inform individuals effectively. The system categorizes emergency types and communicates this information to relevant authorities, enhancing the overall responsiveness and efficiency of emergency management systems.

Methodology

The methodology centers on fine-tuning LLAMA2 and Mistral models using selected datasets. The Turkey Earthquake X Corpus and Emergency-Disaster Messages Dataset were utilized for this purpose. These datasets include a significant amount of emergency-related communications necessary for training. The LLAMA2 and Mistral models were evaluated on their ability to process this information with accuracy and efficiency across multiple languages through techniques such as supervised fine-tuning, quantization with QLoRA, and techniques like NEFTune for improved performance.

Experimental Results

The paper identifies LLAMA2-70B as the most effective model for classifying emergency messages, achieving high precision and recall rates. It demonstrates superior performance in handling complex, multilingual queries. However, it requires significant computational resources. In contrast, smaller models like LLAMA2-13B offer a balance of speed and accuracy, making them ideal for real-time applications. These findings highlight the trade-offs between model size and performance, emphasizing the importance of selecting appropriate models based on specific application requirements.

Challenges and Considerations

Integrating AI into emergency response systems presents challenges such as potential biases, cybersecurity threats, and the need for human oversight. Data and model poisoning are significant concerns, necessitating stringent validation protocols and adversarial training to maintain system integrity. Moreover, the development of such systems must prioritize ethical considerations, including transparency, privacy, and accountability, to gain public trust and acceptance.

Conclusion

The research underscores the transformative potential of LLMs in crisis management, presenting scalable frameworks for enhanced 911 dispatch and public collaboration. While LLAMA2 shows promise in handling complex real-time language processing, future research should address ethical concerns and ensure responsible AI deployment. The evolution of generative AI and its integration into emergency systems call for ongoing evaluation and refinement to optimize their role in safeguarding public welfare during crises.