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

Abstract

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-lev