Instructor–Worker large language model system for policy recommendation: A case study on air quality analysis of the January 2025 Los Angeles wildfires - Kyle Gao; Dening Lu; Liangzhi Li; Nan Chen; Hongjie He; Jing Du; Linlin Xu; Jonathan Li (International Journal of Applied Earth Observations and Geoinformation)
The Los Angeles wildfires of January 2025 caused more than 250 billion dollars in damage and lasted for nearly an entire month before containment. Following our previous work, the Digital Twin Building, we modify and leverage the multi-agent Large Language Model (LLM) framework as well as the cloud-mapping integration to study the air quality during the Los Angeles wildfires. Recent advances in large language models have allowed for out-of-the-box automated large-scale data analysis. We use a multi-agent large language system comprised of an Instructor agent and Worker agents. Upon receiving the users’ instructions, the Instructor agent retrieves the data from the cloud platform and produces instruction prompts to the Worker agents. The Worker agents then analyze the data and provide summaries. The summaries are finally input back into the Instructor agent, which then provides the final data analysis. We test this system’s capability for data-based policy recommendation by assessing our Large Language Model System with Instructor–Worker Architecture’s health recommendations and numerical summarizations based on the air quality data during the Los Angeles wildfires.