arXiv:2606.03363v1 Announce Type: new Abstract: Text-to-SQL enables natural language access to databases, and recent LLMs have substantially advanced its capabilities. Existing benchmarks such as Spider, BIRD, and Spider~2.0 evaluate schema generalization, large-scale databases, and realistic workflows, but largely overlook enterprise scenarios where SQL generation depends on private business knowledge, such as internal metrics, reporting conventions, and organizational rules. We introduce EntSQL, an enterprise-oriented Text-to-SQL benchmark for evaluating long-context grounding over proprieta
The proliferation of advanced LLMs and their application to enterprise data highlights the critical need for secure, accurate, and context-aware natural language interfaces for proprietary information.
This development addresses a key obstacle for AI adoption in regulated and data-sensitive industries by providing a framework for grounding LLMs in private enterprise knowledge, unlocking new efficiencies and insights.
The focus shifts from generic Text-to-SQL benchmarks to those specifically designed for long-context grounding in proprietary enterprise knowledge, improving the practical utility and trustworthiness of AI applications within businesses.
- · Enterprise AI providers
- · Large language model developers
- · Data-intensive enterprise sectors
- · Internal IT/Data teams
- · Generic SQL query tools
- · Companies with poor data governance
- · LLMs lacking grounding capabilities
Enterprises can more effectively and securely leverage natural language for internal data querying, reducing reliance on specialized technical staff.
Increased adoption of customized LLMs for internal business intelligence could lead to a proliferation of enterprise-specific AI agents.
The enhanced ability to query private knowledge bases via natural language could accelerate the automation of complex white-collar tasks, potentially impacting workforce composition in data analysis and reporting roles.
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Read at arXiv cs.CL