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

📰 ArXiv cs.AI

Learn how GROUND reduces hallucinations in LLM-based enterprise analytics by using governed semantic definitions, improving the accuracy of natural-language analytics over enterprise data warehouses.

advanced Published 28 Aug 2026

Action Steps

Apply governed semantic definitions to LLM-based enterprise analytics using GROUND
Configure database schemas to align with approved business metrics and dimensions
Test LLM-generated queries against governed semantic definitions to detect hallucinations
Run GROUND to reduce hallucinated metrics and invalid joins in enterprise reporting
Compare results with and without GROUND to evaluate its effectiveness in improving analytics accuracy

Who Needs to Know This

Data scientists and analysts working with large enterprise data warehouses can benefit from GROUND to improve the accuracy of their natural-language analytics and reduce hallucinations.

Key Insight

💡 Governed semantic definitions can significantly reduce hallucinations in LLM-based enterprise analytics, improving the accuracy of natural-language analytics over enterprise data warehouses.

Full Article

Title: 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

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