Navigating the Knowledge Sea: Planet-scale answer retrieval using LLMs
Dipankar Sarkar
TL;DR
The paper addresses the challenge of delivering accurate, direct answers at web scale by bridging traditional information retrieval with answer-focused retrieval through Large Language Models (LLMs). It surveys historical IR milestones, the rise of Retrieval Augmented Generation (RAG), and the move from link-centric results to context-aware, answer-oriented systems. A key contribution is a proposed planet-scale answer retrieval pipeline that combines LLMs with planet-scale indexes, coupled with LLM-based indexing and selective filtering to improve relevance and quality. The work highlights practical implications for search systems and knowledge platforms, while underscoring ongoing concerns around accuracy, bias, and ethics in AI-driven information access.
Abstract
Information retrieval is a rapidly evolving field of information retrieval, which is characterized by a continuous refinement of techniques and technologies, from basic hyperlink-based navigation to sophisticated algorithm-driven search engines. This paper aims to provide a comprehensive overview of the evolution of Information Retrieval Technology, with a particular focus on the role of Large Language Models (LLMs) in bridging the gap between traditional search methods and the emerging paradigm of answer retrieval. The integration of LLMs in the realms of response retrieval and indexing signifies a paradigm shift in how users interact with information systems. This paradigm shift is driven by the integration of large language models (LLMs) like GPT-4, which are capable of understanding and generating human-like text, thus enabling them to provide more direct and contextually relevant answers to user queries. Through this exploration, we seek to illuminate the technological milestones that have shaped this journey and the potential future directions in this rapidly changing field.