- The paper provides a comprehensive survey and taxonomy of how large language models (LLMs) are used across mitigation, preparedness, response, and recovery phases of disaster management.
- LLMs are applied for tasks like vulnerability assessment, public awareness, real-time response, and recovery planning, leveraging different model architectures including encoder-based, decoder-based, and multimodal types.
- Key challenges include data scarcity, computational demands, and potential for hallucinated outputs, highlighting the need for tailored models and practical application in disaster contexts.
Harnessing LLMs for Disaster Management: A Survey
The paper "Harnessing LLMs for Disaster Management: A Survey" by Zhenyu Lei et al. provides an exhaustive review of how LLMs are applied across various stages of disaster management. This detailed survey presents a comprehensive taxonomy based on disaster phases and application scenarios, aiming to fill the existing gap in systematic reviews of the integration of LLMs in this critical area.
Summary of Findings
The authors classify the disaster management spectrum into four distinct phases: mitigation, preparedness, response, and recovery. Each phase benefits from different architectures of LLMs, including encoder-based models like BERT, decoder-based models such as GPT, and multimodal variations integrating multiple data modalities. The survey meticulously categorizes the LLM applications and highlights methodologies employed in each disaster phase, steering discussions towards novel insights and sectorial advancements.
Disaster Mitigation
In the mitigation phase, LLMs are leveraged primarily for vulnerability assessments. The paper highlights their usage in classifying vulnerabilities and generating answers pertinent to infrastructure and community vulnerabilities. Encoder-based LLMs facilitate the classification of infrastructure vulnerabilities, while decoder-based models assist in responding to community-centric inquiries.
Disaster Preparedness
For preparedness, LLMs enhance public awareness, support disaster forecasting, and aid in evacuation planning. Public awareness is enhanced through encoder-based models that extract critical knowledge from diverse data sources and decoder-based models that improve reasoning and provide answers to disaster-related queries. Disaster forecasting employs LLMs for occurrence classification by extrapolating data from social media and integrating various modalities. Additionally, LLMs assist in generating structured evacuation plans to ensure safe operations during disasters.
Disaster Response
In the response phase, LLMs play a crucial role in real-time disaster identification, situational assessment, information coordination, and rescue operations. Encoder-based models classify relevant disaster-related information, while advanced modalities and ensemble methods enhance detection accuracy. Multimodal LLMs aid in assessing disaster spread and severity using various data inputs. Furthermore, LLMs are used for processing requests for help, locating them, and summarizing or generating reports based on comprehensive datasets.
Disaster Recovery
In recovery, LLMs are essential for impact assessment, recovery plan generation, and tracking recovery processes. These models assess damage and loss across physical and social dimensions, enhance understanding through sentiment analysis, and aid in plan formulation for rebuilding efforts post-disaster.
Applications and Challenges
The survey underscores that while response phase applications of LLMs are extensively explored, there is an imperative need for diversification across other phases like mitigation and recovery. It also highlights a disparity between the extensive LLM research and practical application in disaster-specific contexts, thus advocating for models tailored to address specific disaster management challenges.
The key challenges identified involve data scarcity, especially outside classification tasks, the latency and computational demands of deploying large LLMs, and the potential for hallucinated outputs from generative models, which can have serious implications within disaster contexts. The authors suggest potential solutions, such as synthetic data generation, development of lightweight models, and integration of retrieval-augmented generation techniques to mitigate these issues.
Implications and Future Directions
The findings of this paper have significant theoretical and practical implications, bridging the gap between AI advancements and disaster management needs. It calls for collaborative efforts among AI researchers, policymakers, and practitioners to leverage the full potential of LLMs in building resilient communities and improving disaster management practices. Future research should focus on expanding the application of LLMs across all disaster management phases and addressing identified challenges to optimize the effectiveness of LLMs in disaster contexts.
In conclusion, this survey not only provides a robust framework for understanding the current landscape of LLM applications in disaster management but also sets the stage for future explorations in this critical field.
