# A Survey of Conversational Search

arXiv: https://arxiv.org/abs/2410.15576 · HTML: https://mlsanity.com/paper/2410.15576/

## 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 (most cited first, 30 of 185)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [Contrastive Learning with Hard Negative Samples](https://mlsanity.com/paper/2010.04592/) (2020)
- [Enabling Large Language Models to Generate Text with Citations](https://mlsanity.com/paper/2305.14627/) (2023)
- [Wizard of Wikipedia: Knowledge-Powered Conversational agents](https://mlsanity.com/paper/1811.01241/) (2018)
- [RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2010.08191/) (2020)
- [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023)
- [BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains](https://mlsanity.com/paper/2402.10373/) (2024)
- [RARR: Researching and Revising What Language Models Say, Using Language Models](https://mlsanity.com/paper/2210.08726/) (2022)
- [SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking](https://mlsanity.com/paper/2107.05720/) (2021)
- [MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data](https://mlsanity.com/paper/2304.08247/) (2023)
- [The Power of Noise: Redefining Retrieval for RAG Systems](https://mlsanity.com/paper/2401.14887/) (2024)
- [RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation](https://mlsanity.com/paper/2310.04408/) (2023)
- [Query Rewriting for Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2305.14283/) (2023)
- [ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering](https://mlsanity.com/paper/2210.03849/) (2022)
- [RA-DIT: Retrieval-Augmented Dual Instruction Tuning](https://mlsanity.com/paper/2310.01352/) (2023)
- [Towards Deep Conversational Recommendations](https://mlsanity.com/paper/1812.07617/) (2018)
- [Med-HALT: Medical Domain Hallucination Test for Large Language Models](https://mlsanity.com/paper/2307.15343/) (2023)
- [Query2doc: Query Expansion with Large Language Models](https://mlsanity.com/paper/2303.07678/) (2023)
- [Open-Domain Question Answering Goes Conversational via Question Rewriting](https://mlsanity.com/paper/2010.04898/) (2020)
- [A Survey on Conversational Recommender Systems](https://mlsanity.com/paper/2004.00646/) (2020)
- [Asking Clarifying Questions in Open-Domain Information-Seeking Conversations](https://mlsanity.com/paper/1907.06554/) (2019)
- [A Survey of Large Language Models in Medicine: Progress, Application, and Challenge](https://mlsanity.com/paper/2311.05112/) (2023)

## Built on by (most cited first, 22 of 22)

- [From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents](https://mlsanity.com/paper/2506.18959/) (2025)
- [Search Arena: Analyzing Search-Augmented LLMs](https://mlsanity.com/paper/2506.05334/) (2025)
- [Benchmarking Poisoning Attacks against Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.18543/) (2025)
- [LaSER: Internalizing Explicit Reasoning into Latent Space for Dense Retrieval](https://mlsanity.com/paper/2603.01425/) (2026)
- [MORTAR: Multi-turn Metamorphic Testing for LLM-based Dialogue Systems](https://mlsanity.com/paper/2412.15557/) (2024)
- [Contextualizing Search Queries In-Context Learning for Conversational Rewriting with LLMs](https://mlsanity.com/paper/2502.15009/) (2025)
- [Enhancing Risk Assessment in Transformers with Loss-at-Risk Functions](https://mlsanity.com/paper/2411.02558/) (2024)
- [Superintelligent Retrieval Agent: The Next Frontier of Agentic Retrieval](https://mlsanity.com/paper/2605.06647/) (2026)
- [GenIR: Generative Visual Feedback for Mental Image Retrieval](https://mlsanity.com/paper/2506.06220/) (2025)
- [From Answers to Guidance: A Proactive Dialogue System for Legal Documents](https://mlsanity.com/paper/2510.19723/) (2025)
- [Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2601.13115/) (2026)
- [Analytical Search](https://mlsanity.com/paper/2602.11581/) (2026)
- [DuCCAE: A Hybrid Engine for Immersive Conversation via Collaboration, Augmentation, and Evolution](https://mlsanity.com/paper/2603.19248/) (2026)
- [CroSearch-R1: Better Leveraging Cross-lingual Knowledge for Retrieval-Augmented Generation](https://mlsanity.com/paper/2604.25182/) (2026)
- [ElectriQ: A Benchmark for Assessing the Response Capability of Large Language Models in Power Marketing](https://mlsanity.com/paper/2507.22911/) (2025)
- [From Events to Trending: A Multi-Stage Hotspots Detection Method Based on Generative Query Indexing](https://mlsanity.com/paper/2601.05258/) (2025)
- [Beyond Pipelines: A Fundamental Study on the Rise of Generative-Retrieval Architectures in Web Research](https://mlsanity.com/paper/2602.17450/) (2026)
- [Sensitivity-Aware Retrieval-Augmented Intent Clarification](https://mlsanity.com/paper/2603.06025/) (2026)
- [Learning to Route Queries to Heads for Attention-based Re-ranking with Large Language Models](https://mlsanity.com/paper/2604.24608/) (2026)
- [Seeking Information with RAG-Assistants: Does Model Size Matter in Human-AI Collaborations?](https://mlsanity.com/paper/2605.00964/) (2026)
- [Interactive Multi-Turn Retrieval for Health Videos](https://mlsanity.com/paper/2605.01409/) (2026)
- [Improving Ad-hoc Search Effectiveness for Conversational Information Retrieval via Model Merging](https://mlsanity.com/paper/2607.08540/) (2026)

## 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.