A New Query Expansion Approach via Agent-Mediated Dialogic Inquiry

• URL: http://arxiv.org/abs/2502.08557v3
• Date: Thu, 14 Aug 2025 05:37:14 GMT
• Title: A New Query Expansion Approach via Agent-Mediated Dialogic Inquiry
• Authors: Wonduk Seo, Hyunjin An, Seunghyun Lee,
• Abstract summary: We propose AMD: a new Agent-Mediated Dialogic Framework that engages in a dialogic inquiry involving three specialized roles.<n>By leveraging a multi-agent process, AMD effectively crafts richer query representations through inquiry and feedback refinement.
• Score: 10.76224743599566
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Query expansion is widely used in Information Retrieval (IR) to improve search outcomes by supplementing initial queries with richer information. While recent Large Language Model (LLM) based methods generate pseudo-relevant content and expanded terms via multiple prompts, they often yield homogeneous, narrow expansions that lack the diverse context needed to retrieve relevant information. In this paper, we propose AMD: a new Agent-Mediated Dialogic Framework that engages in a dialogic inquiry involving three specialized roles: (1) a Socratic Questioning Agent reformulates the initial query into three sub-questions, with each question inspired by a specific Socratic questioning dimension, including clarification, assumption probing, and implication probing, (2) a Dialogic Answering Agent generates pseudo-answers, enriching the query representation with multiple perspectives aligned to the user's intent, and (3) a Reflective Feedback Agent evaluates and refines these pseudo-answers, ensuring that only the most relevant and informative content is retained. By leveraging a multi-agent process, AMD effectively crafts richer query representations through inquiry and feedback refinement. Extensive experiments on benchmarks including BEIR and TREC demonstrate that our framework outperforms previous methods, offering a robust solution for retrieval tasks.

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