Facilitating Generative Retrieval with Logical Denoising for Interpretable Conversational Search

Conversational search allows multi-turn user-system interactions to support complex information seeking. It is challenging due to frequent topic shifts and ambiguous intentions in conversation. Although existing methods attempt to optimize retrieval performance through query rewriting or session encoding, they still face crucial challenges, including (1) Noisy context: the diverse types of noises in the conversation context prevent models from achieving reliable and consistent contextual understanding; (2) Poor interpretability: the lack of transparency in how results are generated undermines user trust, hindering the practical application of conversational search systems. In this paper, we propose LogiCGR, a novel framework that utilizes curriculum learning and group relative policy optimization (GRPO) to perform logic-enhanced retrieval, improving the robustness and interpretability of conversational search. Specifically, LogiCGR equips large language models (LLMs) with logical denoising and generative retrieval, integrating them seamlessly through an adaptive framework. Additionally, we introduce a lightweight module that works with generative retrieval for self-dual-path retrieval, thus delivering complementary performance gains. Extensive experiments and intuitive case studies demonstrate that our proposed LogiCGR outperforms state-of-the-art baselines in both retrieval performance and interpretability. The code and data are available at https://github.com/GenIRAG/LogiCGR.