Building an On-Premises Knowledge Repository with Large Language Models for Instant Information Access
Year: 2024
Type: article
Abstract: This project aims to design and develop a live knowledge library utilizing large language models (LLMs) to enhance access to real-time information across various domains. The system will be deployed on-premises, enabling instant responses to user queries, thus optimizing information retrieval processes. By leveraging the natural language processing (NLP) capabilities of LLMs, the project seeks to improve decision-making and operational efficiency within organizations. It addresses the growing need for rapid information access, providing precise and accurate answers to user inquiries, minimizing the delays inherent in traditional search methods. Additionally, the system enhances user experience by offering a user-friendly interface with quick response times, making information retrieval more intuitive. The project also focuses on improving internal knowledge flow by facilitating better communication and collaboration across departments. With an emphasis on scalability, the solution is designed to be adaptable to various sectors, ensuring widespread applicability. By continuously learning and adapting to new data, the system will provide up-to-date information, reducing reliance on manual updates and minimizing human error. Ultimately, this innovation aims to significantly enhance productivity, support effective decision-making, and offer a competitive advantage to organizations through the use of AI-driven knowledge management solutions. Keywords: Knowledge Library, Large Language Models, AI, Real-time Information Retrieval, Decision-making
Authors: Burak Dobur, Engin Bıçakcı, Asli Terim, Cemal Arık
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4406420299