arXiv
Navigating the Knowledge Sea: Planet-scale answer retrieval using LLMs
Dipankar Sarkar
Abstract
This paper explores novel approaches to large-scale information retrieval using Large Language Models (LLMs). We investigate methods for efficient answer retrieval across vast knowledge bases, addressing the challenges of accuracy, scalability, and computational efficiency in planet-scale information systems. Our work contributes to the growing field of LLM-powered search and retrieval mechanisms, offering insights into building more effective knowledge navigation systems.
This research investigates the application of Large Language Models (LLMs) in developing scalable solutions for information retrieval across extensive knowledge bases. The work addresses fundamental challenges in managing and retrieving information at a planetary scale, combining the power of modern language models with efficient retrieval mechanisms.
The paper presents novel approaches to answer retrieval, focusing on both accuracy and computational efficiency. Our findings contribute to the broader understanding of how LLMs can be leveraged to navigate and extract meaningful information from vast knowledge repositories.
The complete paper is available on arXiv.