GROUND: Reducing Hallucinations in LLM-Based Enterprise Analytics Through Governed Semantic Definitions

arXiv cs.AIAugust 28, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2608.26157v1 Announce Type: new Abstract: Natural-language analytics over enterprise data warehouses is increasingly important, but production use is limited by hallucinated metrics, invalid joins, wrong grain, unsafe data access, and unsupported explanations. Existing text-to-SQL systems often ground generation in database schemas or retrieved documentation, while enterprise reporting also requires governed business semantics: approved metrics, dimensions, join paths, filters, and row-level security. This paper introduces GROUND, Governed Retrieval Over Unified Normalized Definitions, a framework that constrains LLM-generated analytics to a governed semantic layer. GROUND supplies app

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