Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agents
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 informat...
| Authors | Kaiqiao Han, LuAn Tang, Renliang Sun et al. |
|---|---|
| Primary category | cs.CL |
| Published | 23 May 2026 |
| Last updated at source | 23 May 2026 |
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