Recent advancement of large language models (LLMs) represents a transformational capability at the frontier of artificial intelligence. However, LLMs are generalized models, trained on extensive text corpus, and often struggle to provide context-specific information, particularly in areas requiring specialized knowledge, such as wildfire details within the broader context of climate change. For decision-makers focused on wildfire resilience and adaptation, it is crucial to obtain responses that are not only precise but also domain-specific. To that end, we developed WildfireGPT, a prototype LLM agent designed to transform user queries into actionable insights on wildfire risks. We enrich WildfireGPT by providing additional context, such as climate projections and scientific literature, to ensure its information is current, relevant, and scientifically accurate. This enables WildfireGPT to be an effective tool for delivering detailed, user-specific insights on wildfire risks to support a diverse set of end users, including but not limited to researchers and engineers, for making positive impact and decision making.
@article{arxiv.2402.07877,
title = {WildfireGPT: Tailored Large Language Model for Wildfire Analysis},
author = {Yangxinyu Xie and Bowen Jiang and Tanwi Mallick and Joshua David Bergerson and John K. Hutchison and Duane R. Verner and Jordan Branham and M. Ross Alexander and Robert B. Ross and Yan Feng and Leslie-Anne Levy and Weijie Su and Camillo J. Taylor},
journal= {arXiv preprint arXiv:2402.07877},
year = {2025}
}
Comments
restoring content for arXiv:2402.07877v2 which was replaced in error