Document Type
Article
Abstract
Systems for crisis response have required several different models for the analysis of unstructured text, such as identifying needs, locations, topics, routing, and matching of needs with available responders. Large Language Models (LLMs) have replaced task-specific models across various language processing tasks. However, LLMs are known to be limited by their training data, collected before the crisis. In this demo, we explore the use of LLMs for crisis response scenarios with rapidly evolving information environments. We show how the augmentation of these models with external reliable sources of crisis-specific information can help build adaptive systems for response. The demonstration video can be found at: https://youtu.be/jKeU5WsG20o.
Digital Object Identifier (DOI)
Publication Info
Published in IEEE International Conference on Data Mining (ICDM 2025), 2025.
APA Citation
Lo, K.-C., Maneriker, P., Ganesh, S., Winecki, D., Garrett, K., Hyder, A., Nandi, A., Shalin, V., Bowen, S., Sheth, A., & Parthasarathy, S. (2025). Crisis observatory: extracting credible signals during a crisis in the age of LLMs. IEEE International Conference on Data Mining (ICDM 2025). https://doi.org/10.1109/ICDMW69685.2025.00333
Rights
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