A new framework called GROUND has been developed to reduce hallucinations in large language model (LLM)-based enterprise analytics. GROUND enforces governed semantic definitions, ensuring that generated SQL queries adhere to approved metrics, dimensions, join paths, and security policies before execution. In benchmarks using synthetic and real-world data, GROUND demonstrated zero measured hallucinations across six categories and prevented violations of row-level security, outperforming systems that relied solely on database schemas or retrieval-augmented generation. AI
IMPACT This framework could enable more reliable and secure use of LLMs for enterprise data analysis by mitigating common hallucination issues.
RANK_REASON The cluster contains a research paper detailing a new framework for LLM-based analytics. [lever_c_demoted from research: ic=1 ai=1.0]
- Aravind Sasidharan Pillai
- hallucinations
- LLM
- National Highway Traffic Safety Administration
- retrieval-augmented generation
- semantic definitions
- SQL
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