  The development of plans of action in disaster response scenarios is a
time-consuming process. Large Language Models (LLMs) offer a powerful solution
to expedite this process through in-context learning. This study presents
DisasterResponseGPT, an algorithm that leverages LLMs to generate valid plans
of action quickly by incorporating disaster response and planning guidelines in
the initial prompt. In DisasterResponseGPT, users input the scenario
description and receive a plan of action as output. The proposed method
generates multiple plans within seconds, which can be further refined following
the user's feedback. Preliminary results indicate that the plans of action
developed by DisasterResponseGPT are comparable to human-generated ones while
offering greater ease of modification in real-time. This approach has the
potential to revolutionize disaster response operations by enabling rapid
updates and adjustments during the plan's execution.
