EntSQL: A Benchmark for Grounding Text-to-SQL in Long-Context Enterprise Knowledge
For enterprise users needing SQL generation from private business knowledge, this benchmark reveals a critical gap in current LLM capabilities.
EntSQL introduces a benchmark for Text-to-SQL in enterprise settings, where queries require grounding in proprietary business documents. The best system achieves only 15.9% accuracy on English inputs, highlighting the challenge.
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 proprietary business documents. EntSQL contains 1,066 aligned Chinese-English semantic examples across five business domains, with most examples requiring domain knowledge beyond the question and schema and involving complex SQL structures. On English inputs, the best evaluated system reaches only 15.9\% when long-form documents are provided, highlighting the difficulty of grounding SQL generation in enterprise knowledge.