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

arXiv: https://arxiv.org/abs/2306.17271 · HTML: https://mlsanity.com/paper/2306.17271/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 64 references that are in the map.
- First posted on arXiv 29 Jun 2023.

## Builds on (most cited first, 10 of 10)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)
- [An Explanation of In-context Learning as Implicit Bayesian Inference](https://mlsanity.com/paper/2111.02080/) (2021)
- [How Many Data Points is a Prompt Worth?](https://mlsanity.com/paper/2103.08493/) (2021)
- [Graph Neural Network Enhanced Language Models for Efficient Multilingual Text Classification](https://mlsanity.com/paper/2203.02912/) (2022)

## Built on by (most cited first, 30 of 34)

- [UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility](https://mlsanity.com/paper/2501.02341/) (2025)
- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [Multi-Agent Geospatial Copilots for Remote Sensing Workflows](https://mlsanity.com/paper/2501.16254/) (2025)
- [Harnessing Large Language Models for Disaster Management: A Survey](https://mlsanity.com/paper/2501.06932/) (2025)
- [From Text to Transformation: A Comprehensive Review of Large Language Models' Versatility](https://mlsanity.com/paper/2402.16142/) (2024)
- [On Large Language Models in National Security Applications](https://mlsanity.com/paper/2407.03453/) (2024)
- [Enhancing Emergency Decision-making with Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2311.08732/) (2023)
- [COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations](https://mlsanity.com/paper/2402.01786/) (2024)
- [LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey](https://mlsanity.com/paper/2402.14558/) (2024)
- [Geo-OLM: Enabling Sustainable Earth Observation Studies with Cost-Efficient Open Language Models & State-Driven Workflows](https://mlsanity.com/paper/2504.04319/) (2025)
- [DisasterQA: A Benchmark for Assessing the performance of LLMs in Disaster Response](https://mlsanity.com/paper/2410.20707/) (2024)
- [Empowering LLM Agents with Geospatial Awareness: Toward Grounded Reasoning for Wildfire Response](https://mlsanity.com/paper/2510.12061/) (2025)
- [Towards Evaluating Proactive Risk Awareness of Multimodal Language Models](https://mlsanity.com/paper/2505.17455/) (2025)
- [A RAG-Based Multi-Agent LLM System for Natural Hazard Resilience and Adaptation](https://mlsanity.com/paper/2504.17200/) (2025)
- [RESPONSE: Benchmarking the Ability of Language Models to Undertake Commonsense Reasoning in Crisis Situation](https://mlsanity.com/paper/2503.11348/) (2025)
- [IncidentResponseGPT: Generating Traffic Incident Response Plans with Generative Artificial Intelligence](https://mlsanity.com/paper/2404.18550/) (2024)
- [No Size Fits All: The Perils and Pitfalls of Leveraging LLMs Vary with Company Size](https://mlsanity.com/paper/2408.01444/) (2024)
- [Reimagining Urban Science: Scaling Causal Inference with Large Language Models](https://mlsanity.com/paper/2504.12345/) (2025)
- [Scalable Interactive Machine Learning for Future Command and Control](https://mlsanity.com/paper/2402.06501/) (2024)
- [Automating the loop in traffic incident management on highway](https://mlsanity.com/paper/2503.12085/) (2025)
- [From Prediction to Foresight: The Role of AI in Designing Responsible Futures](https://mlsanity.com/paper/2511.21570/) (2025)
- [Adaptive Multilevel Twisted Sequential Monte Carlo for Rare Events Estimation in Language Models](https://mlsanity.com/paper/2608.21736/) (2026)
- [GeoGrid-Bench: Can Foundation Models Understand Multimodal Gridded Geo-Spatial Data?](https://mlsanity.com/paper/2505.10714/) (2025)
- [Building Safer Sites: A Large-Scale Multi-Level Dataset for Construction Safety Research](https://mlsanity.com/paper/2508.09203/) (2025)
- [Risk-Conditioned Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2609.08064/) (2026)
- [ClimaEmpact: Domain-Aligned Small Language Models and Datasets for Extreme Weather Analytics](https://mlsanity.com/paper/2504.19066/) (2025)
- [Automated Traffic Incident Response Plans using Generative Artificial Intelligence: Part 1 -- Building the Incident Response Benchmark](https://mlsanity.com/paper/2506.03381/) (2025)
- [A Dynamic Fusion Model for Consistent Crisis Response](https://mlsanity.com/paper/2509.01053/) (2025)
- [Human-AI Use Patterns for Decision-Making in Disaster Scenarios: A Systematic Review](https://mlsanity.com/paper/2509.12034/) (2025)
- [LEO-RobotAgent: A General-purpose Robotic Agent for Language-driven Embodied Operator](https://mlsanity.com/paper/2512.10605/) (2025)

## Abstract (arXiv)

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.