mlsanityData to 14 Oct 2026

ICR: Iterative Clarification and Rewriting for Conversational Search

arXiv 2509.05100 ↗First posted 5 Sep 2025cs.CL cs.AIcited by 4

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Abstract (arXiv)

Most previous work on Conversational Query Rewriting employs an end-to-end rewriting paradigm. However, this approach is hindered by the issue of multiple fuzzy expressions within the query, which complicates the simultaneous identification and rewriting of multiple positions. To address this issue, we propose a novel framework ICR (Iterative Clarification and Rewriting), an iterative rewriting scheme that pivots on clarification questions. Within this framework, the model alternates between generating clarification questions and rewritten queries. The experimental results show that our ICR can continuously improve retrieval performance in the clarification-rewriting iterative process, thereby achieving state-of-the-art performance on two popular datasets.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 17 of its 25 references that are in the map. First posted on arXiv 5 Sep 2025.

Builds on

17 of 25 references.

References · 17

  1. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  2. 2017Billion-scale similarity search with GPUscited by 2,497
  3. 2020Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrievalcited by 849
  4. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 517
  5. 2023Fine-Tuning LLaMA for Multi-Stage Text Retrievalcited by 272
  6. 2020Open-Domain Question Answering Goes Conversational via Question Rewritingcited by 148
  7. 2021TopiOCQA: Open-domain Conversational Question Answering with Topic Switchingcited by 96
  8. 2023Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Searchcited by 75
  9. 2023Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewritingcited by 69
  10. 2021CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learningcited by 68
  11. 2020TREC CAsT 2019: The Conversational Assistance Track Overviewcited by 64
  12. 2023ConvGQR: Generative Query Reformulation for Conversational Searchcited by 59
5 more
  1. 2020Conversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Modelscited by 45
  2. 2024CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Searchcited by 29
  3. 2024Ask Optimal Questions: Aligning Large Language Models with Retriever's Preference in Conversationcited by 23
  4. 2023IterCQR: Iterative Conversational Query Reformulation with Retrieval Guidancecited by 20
  5. 2024AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignmentcited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Think Then Embed: Generative Context Improves Multimodal Embeddingcited by 41
  2. 2026Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsingcited by 0
  3. 2026Factorized Hypothesis Search for Evidence-to-Taxonomy Retrievalcited by 0
  4. 2026CompCQR: Compositional Query Generation for Training-Free Conversational Searchcited by 0