# Generating Multi-Aspect Queries for Conversational Search

arXiv: https://arxiv.org/abs/2403.19302 · HTML: https://mlsanity.com/paper/2403.19302/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2024.

## Built on by (most cited first, 7 of 7)

- [A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges](https://mlsanity.com/paper/2508.05668/) (2025)
- [DiSCo: LLM Knowledge Distillation for Efficient Sparse Retrieval in Conversational Search](https://mlsanity.com/paper/2410.14609/) (2024)
- [Generative Information Retrieval Evaluation](https://mlsanity.com/paper/2404.08137/) (2024)
- [IRLab@iKAT24: Learned Sparse Retrieval with Multi-aspect LLM Query Generation for Conversational Search](https://mlsanity.com/paper/2411.14739/) (2024)
- [A Multi-Agent Perspective on Modern Information Retrieval](https://mlsanity.com/paper/2502.14796/) (2025)
- [Investigating LLM Variability in Personalized Conversational Information Retrieval](https://mlsanity.com/paper/2510.03795/) (2025)
- [uva-irlab-conv at SemEval-2026 Task 8: Multi-Turn RAG with Learned Sparse Retrieval and Listwise Reranking](https://mlsanity.com/paper/2606.11945/) (2026)

## Abstract (arXiv)

Conversational information seeking (CIS) systems aim to model the user's information need within the conversational context and retrieve the relevant information. One major approach to modeling the conversational context aims to rewrite the user utterance in the conversation to represent the information need independently. Recent work has shown the benefit of expanding the rewritten utterance with relevant terms. In this work, we hypothesize that breaking down the information of an utterance into multi-aspect rewritten queries can lead to more effective retrieval performance. This is more evident in more complex utterances that require gathering evidence from various information sources, where a single query rewrite or query representation cannot capture the complexity of the utterance. To test this hypothesis, we conduct extensive experiments on five widely used CIS datasets where we leverage LLMs to generate multi-aspect queries to represent the information need for each utterance in multiple query rewrites. We show that, for most of the utterances, the same retrieval model would perform better with more than one rewritten query by 85% in terms of nDCG@3. We further propose a multi-aspect query generation and retrieval framework, called MQ4CS. Our extensive experiments show that MQ4CS outperforms the state-of-the-art query rewriting methods. We make our code and our new dataset of generated multi-aspect queries publicly available.