Conversational Search: From Fundamentals to Frontiers in the LLM Era

TL;DR

This paper explores the fundamentals and recent advances in conversational search, emphasizing how large language models revolutionize multi-turn, dialogue-based information retrieval systems.

Contribution

It provides a comprehensive overview connecting foundational principles with emerging LLM-driven innovations in conversational search.

Findings

Highlights the role of LLMs in understanding user intent.

Discusses new challenges posed by LLM integration.

Outlines future directions for research and development.

Abstract

Conversational search enables multi-turn interactions between users and systems to fulfill users' complex information needs. During this interaction, the system should understand the users' search intent within the conversational context and then return the relevant information through a flexible, dialogue-based interface. The recent powerful large language models (LLMs) with capacities of instruction following, content generation, and reasoning, attract significant attention and advancements, providing new opportunities and challenges for building up intelligent conversational search systems. This tutorial aims to introduce the connection between fundamentals and the emerging topics revolutionized by LLMs in the context of conversational search. It is designed for students, researchers, and practitioners from both academia and industry. Participants will gain a comprehensive understanding of both the core principles and cutting-edge developments driven by LLMs in conversational search, equipping them with the knowledge needed to contribute to the development of next-generation conversational search systems.