DisasterResponseGPT: Large Language Models for Accelerated Plan of Action Development in Disaster Response Scenarios
Keywords: Generative models; Large Language Models; In-context learning; Human-guided machine learning; Disaster response
TL;DR: We leverage in-context learning in large language models to rapidly generate, together with humans, plans of action for disaster response operations.
Abstract: 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.
Submission Number: 38
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