ConvGQR: Generative Query Reformulation for Conversational Search
Authors: Fengran Mo, Kelong Mao, Yutao Zhu, Yihong Wu, Kaiyu Huang, Jian-Yun Nie Published: 2023-05-25 Source: https://arxiv.org/abs/2305.15645
Overview
This post summarizes the paper *ConvGQR: Generative Query Reformulation for Conversational Search*.
Background and Motivation
In conversational search, the user's real search intent for the current turn depends on the previous conversation history, making it challenging to determine a good search query from the whole conversation context. To avoid the expensive re-training of the query encoder, most existing methods learn a rewriting model to de-contextualize the current query by mimicking manual query rewriting. However, manually rewritten queries are not always the best search queries — training a rewriting model on them limits the model's ability to produce good search queries. Another useful hint is the potential answer to the question.
Method
ConvGQR reformulates conversational queries based on generative pre-trained language models (PLMs), using two components:
1. Query rewriting — a generative model that de-contextualizes the current query given the conversation history; 2. Potential answer generation — a second generative model that produces a plausible answer to the current question.
Combining both signals yields better search queries than rewriting alone. Additionally, a knowledge infusion mechanism relates query reformulation to retrieval performance, jointly optimizing both query reformulation and retrieval.
Evaluation
Extensive experiments on four conversational search datasets demonstrate the effectiveness of ConvGQR. Quantitative results should be verified against the original PDF tables.
Original Abstract
> In conversational search, the user's real search intent for the current turn is dependent on the previous conversation history. It is challenging to determine a good search query from the whole conversation context. To avoid the expensive re-training of the query encoder, most existing methods try to learn a rewriting model to de-contextualize the current query by mimicking the manual query rewriting. However, manually rewritten queries are not always the best search queries. Training a rewriting model on them would limit the model's ability to produce good search queries. Another useful hint is the potential answer to the question. In this paper, we propose ConvGQR, a new framework to reformulate conversational queries based on generative pre-trained language models (PLMs), one for query rewriting and another for generating potential answers. By combining both, ConvGQR can produce better search queries. In addition, to relate query reformulation to retrieval performance, we propose a knowledge infusion mechanism to optimize both query reformulation and retrieval. Extensive experiments on four conversational search datasets demonstrate the effectiveness of ConvGQR.
Related Work
- A Survey of Conversational Search (Sep 2025, ACM)
- Engineering Conversational Search Systems: A Review (arXiv:2407.00997)
- Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning
- CTR-Guided Generative Query Suggestion in Conversational Search (EMNLP)
- ChatRetriever: Adapting LLMs for Generalized and Robust Conversational Dense Retrieval (arXiv:2404.13556)
- CoSearchAgent: A Lightweight Collaborative Search Agent (arXiv:2402.06360)