EvidenceBot: A Privacy-Preserving, Customizable RAG-Based Tool for Enhancing Large Language Model Interactions
Year: 2025
Type: conference-paper
Abstract: Large Language Models (LLMs) have become pivotal in reshaping the world by enabling advanced natural language processing tasks such as document analysis, content generation, and conversational assistance. Their ability to process and generate human-like text has unlocked unprecedented opportunities across different domains such as healthcare, education, finance, and more. However, commercial LLM platforms face several limitations, including data privacy concerns, context size restrictions, lack of parameter configurability, and limited evaluation capabilities. These shortcomings hinder their effectiveness, particularly in scenarios involving sensitive information, large-scale document analysis, or the need for customized output. This underscores the need for a tool that combines the power of LLMs with enhanced privacy, flexibility, and usability.
Authors: Nafiz Imtiaz Khan, Vladimir Filkov
Institution: University of California, Davis
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412704016