---
id: 20260829-T0-03
title: "减少LLM企业分析幻觉：GROUND用语义定义约束查询生成"
title_en: "GROUND Framework Reduces LLM Hallucinations in Enterprise Analytics"
url: https://ai.daily.yangsir.net/daily/20260829-T0-03
issue_date: 2026-08-29
publish_date: 2026-08-28T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2608.26157
---

# 减少LLM企业分析幻觉：GROUND用语义定义约束查询生成

一项名为GROUND的新研究针对LLM在自然语言查询企业数据仓库时产生的幻觉问题。该框架通过引入“受控语义定义”，在查询生成前对指标、连接和粒度进行规范化约束，从而避免无效连接和错误的数据聚合。在内部基准测试中，GROUND将分析查询的准确率从72%提升至91%，且未增加额外延迟。该方案将提升业务人员直接进行数据探索的可靠性，减少对数据工程师的依赖。

## English Version

**GROUND Framework Reduces LLM Hallucinations in Enterprise Analytics**

A new framework called GROUND targets hallucinations in LLM-based enterprise data analytics. It enforces governed semantic definitions before query generation, preventing invalid joins and wrong data grain. In internal benchmarks, query accuracy rose from 72% to 91% without added latency. This makes self-serve business intelligence more reliable, reducing dependence on dedicated data engineers.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2608.26157)

**详情页**：https://ai.daily.yangsir.net/daily/20260829-T0-03

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