mlsanityData to 14 Oct 2026

A Survey of Conversational Search

arXiv 2410.15576 ↗First posted 21 Oct 2024cs.CL cs.IRcited by 22

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Abstract (arXiv)

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.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Builds on 185 of its 298 references that are in the map. First posted on arXiv 21 Oct 2024.

Builds on

185 of 298 references.

References · 185

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38 more
  1. 2020RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answeringcited by 413
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 8; 2026: 12.

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  1. 2025From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agentscited by 51
  2. 2025Search Arena: Analyzing Search-Augmented LLMscited by 34
  3. 2025Benchmarking Poisoning Attacks against Retrieval-Augmented Generationcited by 18
  4. 2026LaSER: Internalizing Explicit Reasoning into Latent Space for Dense Retrievalcited by 9
  5. 2024MORTAR: Multi-turn Metamorphic Testing for LLM-based Dialogue Systemscited by 7
  6. 2025Contextualizing Search Queries In-Context Learning for Conversational Rewriting with LLMscited by 4
  7. 2024Enhancing Risk Assessment in Transformers with Loss-at-Risk Functionscited by 2
  8. 2026Superintelligent Retrieval Agent: The Next Frontier of Agentic Retrievalcited by 2
  9. 2025GenIR: Generative Visual Feedback for Mental Image Retrievalcited by 1
  10. 2025From Answers to Guidance: A Proactive Dialogue System for Legal Documentscited by 1
  11. 2026Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learningcited by 1
  12. 2026Analytical Searchcited by 1
10 more
  1. 2026DuCCAE: A Hybrid Engine for Immersive Conversation via Collaboration, Augmentation, and Evolutioncited by 1
  2. 2026CroSearch-R1: Better Leveraging Cross-lingual Knowledge for Retrieval-Augmented Generationcited by 1
  3. 2025ElectriQ: A Benchmark for Assessing the Response Capability of Large Language Models in Power Marketingcited by 0
  4. 2025From Events to Trending: A Multi-Stage Hotspots Detection Method Based on Generative Query Indexingcited by 0
  5. 2026Beyond Pipelines: A Fundamental Study on the Rise of Generative-Retrieval Architectures in Web Researchcited by 0
  6. 2026Sensitivity-Aware Retrieval-Augmented Intent Clarificationcited by 0
  7. 2026Learning to Route Queries to Heads for Attention-based Re-ranking with Large Language Modelscited by 0
  8. 2026Seeking Information with RAG-Assistants: Does Model Size Matter in Human-AI Collaborations?cited by 0
  9. 2026Interactive Multi-Turn Retrieval for Health Videoscited by 0
  10. 2026Improving Ad-hoc Search Effectiveness for Conversational Information Retrieval via Model Mergingcited by 0