# IterCQR: Iterative Conversational Query Reformulation with Retrieval Guidance

arXiv: https://arxiv.org/abs/2311.09820 · HTML: https://mlsanity.com/paper/2311.09820/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2023.

## Built on by (most cited first, 20 of 20)

- [Adaptive Query Rewriting: Aligning Rewriters through Marginal Probability of Conversational Answers](https://mlsanity.com/paper/2406.10991/) (2024)
- [AssoMem: Scalable Memory QA with Multi-Signal Associative Retrieval](https://mlsanity.com/paper/2510.10397/) (2025)
- [Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA](https://mlsanity.com/paper/2409.15515/) (2024)
- [DiSCo: LLM Knowledge Distillation for Efficient Sparse Retrieval in Conversational Search](https://mlsanity.com/paper/2410.14609/) (2024)
- [MORTAR: Multi-turn Metamorphic Testing for LLM-based Dialogue Systems](https://mlsanity.com/paper/2412.15557/) (2024)
- [ICR: Iterative Clarification and Rewriting for Conversational Search](https://mlsanity.com/paper/2509.05100/) (2025)
- [Omni-Embed-Audio: Leveraging Multimodal LLMs for Robust Audio-Text Retrieval](https://mlsanity.com/paper/2604.18360/) (2026)
- [Conversational Query Reformulation with the Guidance of Retrieved Documents](https://mlsanity.com/paper/2407.12363/) (2024)
- [AdaRewriter: Unleashing the Power of Prompting-based Conversational Query Reformulation via Test-Time Adaptation](https://mlsanity.com/paper/2506.01381/) (2025)
- [Read the Docs Before Rewriting: Equip Rewriter with Domain Knowledge via Continual Pre-training](https://mlsanity.com/paper/2507.00477/) (2025)
- [Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2601.13115/) (2026)
- [Multi-Faceted Self-Consistent Preference Alignment for Query Rewriting in Conversational Search](https://mlsanity.com/paper/2604.06771/) (2026)
- [QueryAgent-R1: Bridging Query Generation and Product Retrieval for E-Commerce Query Recommendation](https://mlsanity.com/paper/2606.05671/) (2026)
- [Multi-turn Conversational AI from Text to Multimodal Interaction: Data, Models, Evaluation, and Open Challenges](https://mlsanity.com/paper/2608.17605/) (2026)
- [CFDA & CLIP at TREC iKAT 2025: Enhancing Personalized Conversational Search via Query Reformulation and Rank Fusion](https://mlsanity.com/paper/2509.15588/) (2025)
- [Can Synthetic Query Rewrites Capture User Intent Better than Humans in Retrieval-Augmented Generation?](https://mlsanity.com/paper/2509.22325/) (2025)
- [When should I search more: Adaptive Complex Query Optimization with Reinforcement Learning](https://mlsanity.com/paper/2601.21208/) (2026)
- [Evaluating the Utility of Grounding Documents with Reference-Free LLM-based Metrics](https://mlsanity.com/paper/2601.23129/) (2026)
- [CORAL: Adaptive Retrieval Loop for Culturally-Aligned Multilingual RAG](https://mlsanity.com/paper/2604.25676/) (2026)
- [CompCQR: Compositional Query Generation for Training-Free Conversational Search](https://mlsanity.com/paper/2609.14646/) (2026)

## Abstract (arXiv)

Conversational search aims to retrieve passages containing essential information to answer queries in a multi-turn conversation. In conversational search, reformulating context-dependent conversational queries into stand-alone forms is imperative to effectively utilize off-the-shelf retrievers. Previous methodologies for conversational query reformulation frequently depend on human-annotated rewrites. However, these manually crafted queries often result in sub-optimal retrieval performance and require high collection costs. To address these challenges, we propose Iterative Conversational Query Reformulation (IterCQR), a methodology that conducts query reformulation without relying on human rewrites. IterCQR iteratively trains the conversational query reformulation (CQR) model by directly leveraging information retrieval (IR) signals as a reward. Our IterCQR training guides the CQR model such that generated queries contain necessary information from the previous dialogue context. Our proposed method shows state-of-the-art performance on two widely-used datasets, demonstrating its effectiveness on both sparse and dense retrievers. Moreover, IterCQR exhibits superior performance in challenging settings such as generalization on unseen datasets and low-resource scenarios.