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
The proliferation of LLMs in conversational applications highlights the urgent need for more robust and reliable RAG methods to handle real-world, noisy data effectively.
Improving RAG's ability to extract and utilize structured information from noisy data directly enhances the reliability, accuracy, and domain-specificity of AI agents, making them more commercially viable.
This advancement shifts RAG from primarily text-based or graph-based approaches to incorporating structured data, specifically tables, potentially reducing hallucinations and inaccuracies in conversational AI.
- · AI-powered customer service providers
- · Enterprises deploying conversational AI
- · LLM developers
- · Data structuring and quality platforms
- · Legacy RAG solutions
- · Companies relying solely on parametric LLM knowledge
Conversational AI agents become significantly more reliable and capable of handling complex, domain-specific queries.
This leads to increased adoption of AI agents across various industries, automating more sophisticated tasks.
The enhanced capability of AI agents could further accelerate the collapse of white-collar workflows, as more roles become augmentable or automatable by these sophisticated systems.
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Read at arXiv cs.CL