# A Surprisingly Simple yet Effective Multi-Query Rewriting Method for Conversational Passage Retrieval

arXiv: https://arxiv.org/abs/2406.18960 · HTML: https://mlsanity.com/paper/2406.18960/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2024.

## Built on by (most cited first, 14 of 14)

- [A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges](https://mlsanity.com/paper/2508.05668/) (2025)
- [Think Then Embed: Generative Context Improves Multimodal Embedding](https://mlsanity.com/paper/2510.05014/) (2025)
- [Contextualizing Search Queries In-Context Learning for Conversational Rewriting with LLMs](https://mlsanity.com/paper/2502.15009/) (2025)
- [PaRT: Enhancing Proactive Social Chatbots with Personalized Real-Time Retrieval](https://mlsanity.com/paper/2504.20624/) (2025)
- [AdaRewriter: Unleashing the Power of Prompting-based Conversational Query Reformulation via Test-Time Adaptation](https://mlsanity.com/paper/2506.01381/) (2025)
- [UiS-IAI@LiveRAG: Retrieval-Augmented Information Nugget-Based Generation of Responses](https://mlsanity.com/paper/2506.22210/) (2025)
- [A Survey of the State-of-the-Art in Conversational Question Answering Systems](https://mlsanity.com/paper/2509.05716/) (2025)
- [Illocutionary Explanation Planning for Source-Faithful Explanations in Retrieval-Augmented Language Models](https://mlsanity.com/paper/2604.06211/) (2026)
- [H-RAG at SemEval-2026 Task 8: Hierarchical Parent-Child Retrieval for Multi-Turn RAG Conversations](https://mlsanity.com/paper/2605.00631/) (2026)
- [Improving Document Retrieval Coherence for Semantically Equivalent Queries](https://mlsanity.com/paper/2508.07975/) (2025)
- [SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels](https://mlsanity.com/paper/2601.09723/) (2025)
- [ReBOL: Retrieval via Bayesian Optimization with Batched LLM Relevance Observations and Query Reformulation](https://mlsanity.com/paper/2603.20513/) (2026)
- [VeriChat: An Agentic Conversational AI Assistant for Hardware Security Verification](https://mlsanity.com/paper/2607.01668/) (2026)
- [CompCQR: Compositional Query Generation for Training-Free Conversational Search](https://mlsanity.com/paper/2609.14646/) (2026)

## Abstract (arXiv)

Conversational passage retrieval is challenging as it often requires the resolution of references to previous utterances and needs to deal with the complexities of natural language, such as coreference and ellipsis. To address these challenges, pre-trained sequence-to-sequence neural query rewriters are commonly used to generate a single de-contextualized query based on conversation history. Previous research shows that combining multiple query rewrites for the same user utterance has a positive effect on retrieval performance. We propose the use of a neural query rewriter to generate multiple queries and show how to integrate those queries in the passage retrieval pipeline efficiently. The main strength of our approach lies in its simplicity: it leverages how the beam search algorithm works and can produce multiple query rewrites at no additional cost. Our contributions further include devising ways to utilize multi-query rewrites in both sparse and dense first-pass retrieval. We demonstrate that applying our approach on top of a standard passage retrieval pipeline delivers state-of-the-art performance without sacrificing efficiency.