AgInsuranceLLMs

Shi, Michael; Rajesh, Saketh; Truong, An; Hilgenberg, Kyle

AgInsuranceLLMs

dc.contributor.author	Shi, Michael	en
dc.contributor.author	Rajesh, Saketh	en
dc.contributor.author	Truong, An	en
dc.contributor.author	Hilgenberg, Kyle	en
dc.date.accessioned	2024-05-09T18:13:13Z	en
dc.date.available	2024-05-09T18:13:13Z	en
dc.date.issued	2024-05-09	en
dc.description	A web application to provide recommendations and answers to questions related to agricultural insurance using Large Language Models. The final report is available in Word and PDF versions in files AgricultureInsuranceLLMsReport.docx and AgricultureInsuranceLLMsReport.pdf. The final presentation is available in PowerPoint and PDF versions in files AgricultureInsuranceLLMsPresentation.pptx and AgricultureInsuranceLLMsPresentation.pdf. License: GNU General Public License v3.0, https://choosealicense.com/licenses/gpl-3.0/	en
dc.description.abstract	Our project is to develop a conversational assistant to aid users in understanding and choosing appropriate agricultural insurance policies. The assistant leverages a Large Language Model (LLM) trained on datasets from the Rainfall Index Insurance Standards Handbook and USDA site information. It is designed to provide clear, easily understood explanations and guidance, helping users navigate their insurance options. The project encompasses the development of an accessible chat interface, backend integration with a Flask API, and the deployment of the assistant on Virginia Tech's Endeavour cluster. Through personalized recommendations and visualizations, the assistant empowers users to make well-informed decisions regarding their insurance needs. Our project report and presentation outline the project's objectives, design, implementation, and lessons learned, highlighting the potential impact of this interactive conversational assistant in simplifying the complex process of selecting agricultural insurance policies.	en
dc.description.sponsorship	Virginia Tech CALS Strategic Plan Advancement 2023 Integrated Internal Competitive Grants Program: Reassessing drought indicators used to issue automated relief payments in the US Crop Insurance Program	en
dc.identifier.uri	https://hdl.handle.net/10919/118937	en
dc.language.iso	en	en
dc.rights	GNU General Public License v3.0	en
dc.rights.uri	https://choosealicense.com/licenses/gpl-3.0/	en
dc.subject	agriculture	en
dc.subject	insurance	en
dc.subject	drought	en
dc.subject	LLMs	en
dc.subject	RAG	en
dc.title	AgInsuranceLLMs	en
dc.type	Presentation	en
dc.type	Report	en