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A Survey of Conversational Search
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As a cornerstone of modern information access, search engines have become indispensable in everyday life. With the rapid advancements in AI and natural language processing (NLP) technologies, particularly large language models (LLMs), search engines have evolved to support more intuitive and intelligent interactions between users and systems. Conversational search, an emerging paradigm for next-generation search engines, leverages natural language dialogue to facilitate complex and precise information retrieval, thus attracting significant attention. Unlike traditional keyword-based search engines, conversational search systems enhance user experience by supporting intricate queries, maintaining context over multi-turn interactions, and providing robust information integration and processing capabilities. Key components such as query reformulation, search clarification, conversational retrieval, and response generation work in unison to enable these sophisticated interactions. In this survey, we explore the recent advancements and potential future directions in conversational search, examining the critical modules that constitute a conversational search system. We highlight the integration of LLMs in enhancing these systems and discuss the challenges and opportunities that lie ahead in this dynamic field. Additionally, we provide insights into real-world applications and robust evaluations of current conversational search systems, aiming to guide future research and development in conversational search.
Forward citations
Cited by 4 Pith papers
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Bridging the Gap: From Ad-hoc to Proactive Search in Conversations
Conv2Query fine-tunes an LLM to convert conversational context into ad-hoc queries, enabling off-the-shelf retrievers to work effectively on proactive search in conversations.
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UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations
A single LLM jointly fine-tuned for conversational dense retrieval and retrieval-augmented generation beats separate retriever-plus-generator pipelines on most test collections, though its headline benchmark was conta...
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Benchmarking Poisoning Attacks against Retrieval-Augmented Generation
A unified benchmark evaluation finds that existing RAG poisoning attacks remain effective on standard QA datasets, drop on expanded knowledge bases, and are only partially mitigated by current defenses.
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Conversational Search: From Fundamentals to Frontiers in the LLM Era
A four-page proposal for a SIGIR 2025 half-day tutorial connecting conversational search fundamentals with LLM-era techniques; it contains no new experiments, data, or results.
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