Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agents

• URL: http://arxiv.org/abs/2605.24366v1
• Date: Sat, 23 May 2026 03:07:33 GMT
• Title: Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agents
• Abstract summary: Structure-aware Retrieval Augmented Generation (SA-RAG) uses tables as an intermediate structured representation to provide a compact and controllable interface.<n>We introduce a quality-aware table metadata generation framework that models metadata normalization and effectiveness.<n>Experiments on two noisy real-world datasets show that SA-RAG significantly outperforms existing RAG baselines.
• Score: 68.52563127206808
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large Language Models (LLMs) have been widely adopted in conversational applications. However, their reliance on parametric knowledge limits reliability in real-world scenarios that require dynamic or domain-specific information. Retrieval-Augmented Generation (RAG) addresses this limitation by incorporating external knowledge during generation, but existing text-based and graph-based RAG methods often struggle with noisy or irrelevant contexts. In this work, we propose Structure-aware Retrieval Augmented Generation (SA-RAG), which uses tables as an intermediate structured representation to provide a compact and controllable interface that reduces noise while preserving essential information. We introduce a quality-aware table metadata generation framework that models metadata normalization and effectiveness, improving metadata quality and downstream performance. Furthermore, we explore both training-free and training-based table generation methods. Generation validation and direct preference optimization further improve table quality while maintaining semantic and structural consistency. Experiments on two noisy real-world datasets show that SA-RAG significantly outperforms existing RAG baselines. Our code is publicly available at a public repository.

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