![[paper] FloodBrain: Flood Disaster Reporting by Web-based Retrieval Augmented Generation with an LLM](/_next/image?url=https%3A%2F%2Fassets.super.so%2F83f6da10-d459-4ba6-a02f-b3c2666933f3%2Fimages%2F62e09ba3-6152-4a5b-bdfd-b732c6fa25a4%2F2024-04-09_10_39_04-2311.02597.pdf.png&w=3840&q=75)
Description
Our pipeline assimilates information from web search results to produce detailed and accurate reports on flood events.
Project type
AILLM
Status
Done
URL
https://arxiv.org/abs/2311.02597
FloodBrain: Flood Disaster Reporting by Web-based Retrieval...
Fast disaster impact reporting is crucial in planning humanitarian assistance. Large Language Models (LLMs) are well known for their ability to write coherent text and fulfill a variety of tasks...
arxiv.org
Submitted to Artificial Intelligence for Humanitarian Assistance and Disaster Response Workshop @Neurips2023. All authors contributed equally to this work.