arXiv:2606.15971v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) offers an effective approach for large language models to access external knowledge. However, existing methods rely on dense similarity retrieval and face inherent limitations in handling structured constraints and multi-hop reasoning. Incorporating knowledge graphs partially alleviates these issues, but at the cost of semantic fragmentation, high maintenance overhead, and difficult incremental updates. This paper introduces SAG (SQLRetrieval Augmented Generation), a structured architecture for retrieval and a
The proliferation of RAG systems and the inherent limitations of current dense similarity retrieval methods for structured queries necessitate advanced solutions to leverage external knowledge effectively.
Improving RAG's ability to handle structured constraints and multi-hop reasoning will significantly enhance LLMs' reliability and utility in complex, data-rich environments, moving beyond simple information retrieval.
Traditional RAG limitations in structured data and multi-hop questions are addressed by integrating SQL-like query capabilities, allowing for more precise and complex information extraction from external data.
- · AI developers
- · Enterprises with complex datasets
- · Database management systems
- · LLM applications
- · Legacy RAG implementations
- · Unstructured data platforms
LLMs can now perform more sophisticated and accurate data querying, reducing hallucination in structured contexts.
This could lead to a rapid expansion of LLM adoption in industries requiring precise data interaction, such as finance and legal.
The increased integration of LLMs with structured databases might accelerate the development of truly autonomous AI agents capable of complex data analysis and decision-making.
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Read at arXiv cs.CL