Multi-Faceted Self-Consistent Preference Alignment for Query Rewriting in Conversational Search

• URL: http://arxiv.org/abs/2604.06771v1
• Date: Wed, 08 Apr 2026 07:38:45 GMT
• Title: Multi-Faceted Self-Consistent Preference Alignment for Query Rewriting in Conversational Search
• Abstract summary: We propose Multi-Faceted Self-Consistent Preference Aligned CQR (MSPA-CQR)<n>We first construct self-consistent preference alignment data from three dimensions (rewriting, retrieval, and response) to generate more diverse rewritten queries.<n>Our experimental results show that our MSPA-CQR is effective in both in- and out-of-distribution scenarios.
• Score: 25.48484246820614
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conversational Query Rewriting (CQR) aims to rewrite ambiguous queries to achieve more efficient conversational search. Early studies have predominantly focused on the rewriting in isolation, ignoring the feedback from query rewrite, passage retrieval and response generation in the rewriting process. To address this issue, we propose Multi-Faceted Self-Consistent Preference Aligned CQR (MSPA-CQR). Specifically, we first construct self-consistent preference alignment data from three dimensions (rewriting, retrieval, and response) to generate more diverse rewritten queries. Then we propose prefix guided multi-faceted direct preference optimization to learn preference information from three different dimensions. The experimental results show that our MSPA-CQR is effective in both in- and out-of-distribution scenarios.

Related papers

• ICR: Iterative Clarification and Rewriting for Conversational Search [25.48484246820614]
  We propose an iterative rewriting scheme that pivots on clarification questions.<n>Within this framework, the model alternates between generating clarification questions and rewritten queries.<n>Our ICR can continuously improve retrieval performance in the clarification-rewriting iterative process.
  arXiv  Detail & Related papers  (2025-09-05T13:37:04Z)
• IRLab@iKAT24: Learned Sparse Retrieval with Multi-aspect LLM Query Generation for Conversational Search [6.974395116689502]
  iKAT 2024 focuses on advancing conversational assistants, able to adapt their interaction and responses from personalized user knowledge. The track incorporates a Personal Textual Knowledge Base (PTKB) alongside Conversational AI tasks, such as passage ranking and response generation.
  arXiv  Detail & Related papers  (2024-11-22T05:18:35Z)
• Adaptive Query Rewriting: Aligning Rewriters through Marginal Probability of Conversational Answers [66.55612528039894]
  AdaQR is a framework for training query rewriting models with limited rewrite annotations from seed datasets and completely no passage label. A novel approach is proposed to assess retriever's preference for these candidates by the probability of answers conditioned on the conversational query.
  arXiv  Detail & Related papers  (2024-06-16T16:09:05Z)
• LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency [65.01402723259098]
  We propose a novel method of query rewrite named LLM-R2, adopting a large language model (LLM) to propose possible rewrite rules for a database rewrite system. Experimental results have shown that our method can significantly improve the query execution efficiency and outperform the baseline methods.
  arXiv  Detail & Related papers  (2024-04-19T13:17:07Z)
• Selecting Query-bag as Pseudo Relevance Feedback for Information-seeking Conversations [76.70349332096693]
  Information-seeking dialogue systems are widely used in e-commerce systems. We propose a Query-bag based Pseudo Relevance Feedback framework (QB-PRF) It constructs a query-bag with related queries to serve as pseudo signals to guide information-seeking conversations.
  arXiv  Detail & Related papers  (2024-03-22T08:10:32Z)
• Ask Optimal Questions: Aligning Large Language Models with Retriever's Preference in Conversation [23.74712435991676]
  RetPO is designed to optimize a language model for reformulating search queries in line with the preferences of the target retrieval systems.<n>We construct a large-scale dataset called Retrievers' Feedback on over 410K query rewrites across 12K conversations.<n>Our resulting model demonstrates superiority on two benchmarks, surpassing the previous state-of-the-art performance of rewrite-then-retrieve approaches.
  arXiv  Detail & Related papers  (2024-02-19T04:41:31Z)
• ConvGQR: Generative Query Reformulation for Conversational Search [37.54018632257896]
  ConvGQR is a new framework to reformulate conversational queries based on generative pre-trained language models. We propose a knowledge infusion mechanism to optimize both query reformulation and retrieval.
  arXiv  Detail & Related papers  (2023-05-25T01:45:06Z)
• Query Rewriting for Retrieval-Augmented Large Language Models [139.242907155883]
  Large Language Models (LLMs) play powerful, black-box readers in the retrieve-then-read pipeline. This work introduces a new framework, Rewrite-Retrieve-Read instead of the previous retrieve-then-read for the retrieval-augmented LLMs.
  arXiv  Detail & Related papers  (2023-05-23T17:27:50Z)
• Session-Aware Query Auto-completion using Extreme Multi-label Ranking [61.753713147852125]
  We take the novel approach of modeling session-aware query auto-completion as an e Multi-Xtreme Ranking (XMR) problem. We adapt a popular XMR algorithm for this purpose by proposing several modifications to the key steps in the algorithm. Our approach meets the stringent latency requirements for auto-complete systems while leveraging session information in making suggestions.
  arXiv  Detail & Related papers  (2020-12-09T17:56:22Z)
• Query Resolution for Conversational Search with Limited Supervision [63.131221660019776]
  We propose QuReTeC (Query Resolution by Term Classification), a neural query resolution model based on bidirectional transformers. We show that QuReTeC outperforms state-of-the-art models, and furthermore, that our distant supervision method can be used to substantially reduce the amount of human-curated data required to train QuReTeC.
  arXiv  Detail & Related papers  (2020-05-24T11:37:22Z)
• Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting [56.268862325167575]
  We tackle conversational passage retrieval (ConvPR) with query reformulation integrated into a multi-stage ad-hoc IR system. We propose two conversational query reformulation (CQR) methods: (1) term importance estimation and (2) neural query rewriting. For the former, we expand conversational queries using important terms extracted from the conversational context with frequency-based signals. For the latter, we reformulate conversational queries into natural, standalone, human-understandable queries with a pretrained sequence-tosequence model.
  arXiv  Detail & Related papers  (2020-05-05T14:30:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.