Navigating the Knowledge Sea: Planet-scale answer retrieval using LLMs

TL;DR

This paper reviews the evolution of information retrieval, emphasizing how large language models like GPT-4 are transforming answer retrieval and indexing, enabling more direct, context-aware responses in search systems.

Contribution

It provides a comprehensive overview of LLM integration in IR, highlighting technological milestones and future directions in answer retrieval and indexing.

Findings

LLMs enable more direct and relevant answer retrieval.

The integration of LLMs signifies a paradigm shift in IR.

Future directions include advanced indexing and response techniques.

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.