Dynamic Fusion of Large Language Models for Crisis Communication

Abstract

People affected by crises increasingly rely on social media platforms for real-time information and assistance. This underscores the need for a robust and reliable approach to provide accurate and timely information to affected individuals. Large Language Models (LLMs), which can understand user queries and generate responses, have the potential to act as assistants in crisis response. We explore various approaches, including instruction prompts, retrieval-augmented generation, and dynamic fusion of LLMs, to generate responses that address the information needs of affected individuals on social media platforms. Experiments demonstrate that the dynamic fusion approach produces better crisis responses across key evaluation dimensions, including professionalism, actionability, empathy, and relevance.