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

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arxiv 2306.17271 v1 pith:6RW4UYUN submitted 2023-06-29 cs.LG

classification cs.LG
keywords actiondisasterdisasterresponsegptplansresponseplandevelopmentlanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Building Safer Sites: A Large-Scale Multi-Level Dataset for Construction Safety Research

    cs.LG 2025-08 conditional novelty 6.0 of 10

    CSDataset links 50,000+ OSHA construction incidents with 100,000+ inspections and violations, and reports that complaint-driven inspections coincide with a 17.3% lower rate of subsequent incidents.

  2. Automated Traffic Incident Response Plans using Generative Artificial Intelligence: Part 1 -- Building the Incident Response Benchmark

    eess.SY 2025-06 conditional novelty 4.0 of 10

    A 200-incident benchmark from PeMS logs compares LLM-generated traffic response plans against manual reference plans; GPT-4o and Grok 2 achieve the lowest Hamming distances.

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