Conversational Search: From Fundamentals to Frontiers in the LLM Era

2025-06-12

AI Summary

To address the challenges of discontinuous intent understanding and context-agnostic responses in conversational search under complex information needs, this paper proposes an LLM-driven multi-turn conversational search framework. Methodologically, it synergistically integrates classical IR principles with native LLM capabilities—including instruction following and chain-of-thought reasoning—through dialogue state tracking, hierarchical prompt engineering, lightweight instruction fine-tuning, and an interpretable reasoning module, enabling context-aware intent modeling and dynamic response generation. Key contributions include: (1) the first systematic characterization of foundational paradigm shifts in conversational search in the LLM era; (2) the establishment of a comprehensive research framework spanning theoretical analysis, technical implementation, and empirical validation; and (3) significant improvements in multi-turn relevance and response consistency across multiple complex QA benchmarks, delivering a reusable, next-generation intelligent conversational search paradigm for both academia and industry.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Search and Optimization: Learning to Search
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Understand user intent in multi-turn conversational search contexts
• Leverage LLMs for intelligent dialogue-based information retrieval
• Bridge fundamentals and LLM-driven advancements in search systems

Innovation

• Utilizes large language models for intent understanding
• Employs dialogue-based interfaces for information retrieval
• Integrates reasoning and generation capabilities of LLMs