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The development of plans of action in disaster response scenarios is atime-consuming process. Large Language Models (LLMs) offer a powerful solutionto expedite this process through in-context learning. This study presentsDisasterResponseGPT, an algorithm that leverages LLMs to generate valid plansof action quickly by incorporating disaster response and planning guidelines inthe initial prompt. In DisasterResponseGPT, users input the scenariodescription and receive a plan of action as output. The proposed methodgenerates multiple plans within seconds, which can be further refined followingthe user's feedback. Preliminary results indicate that the plans of actiondeveloped by DisasterResponseGPT are comparable to human-generated ones whileoffering greater ease of modification in real-time. This approach has thepotential to revolutionize disaster response operations by enabling rapidupdates and adjustments during the plan's execution.

DisasterResponseGPT: Large Language Models for Accelerated Plan of Action Development in Disaster Response Scenarios