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

arXiv:2605.24366v1 Announce Type: new 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

Source: arXiv cs.CL — read the full report at the original publisher.

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