DisasterResponseGPT: Large Language Models for Accelerated Plan of
Action Development in Disaster Response Scenarios
- URL: http://arxiv.org/abs/2306.17271v1
- Date: Thu, 29 Jun 2023 19:24:19 GMT
- Title: DisasterResponseGPT: Large Language Models for Accelerated Plan of
Action Development in Disaster Response Scenarios
- Authors: Vinicius G. Goecks, Nicholas R. Waytowich
- Abstract summary: This study presents DisasterResponseGPT, an algorithm that leverages Large Language Models (LLMs) to generate valid plans of action quickly.
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.
- Score: 3.42658286826597
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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