Addressing Limitations in NL2SQL Benchmarking

Existing NL2SQL benchmarks often fail to capture the complexities of real-world enterprise database environments. ESQ-Bench introduces a multi-tier evaluation framework specifically designed to measure two critical failure modes: dialect generalization and silent semantic divergence. While many models perform well on standard datasets, they frequently struggle when faced with the subtle syntax variations and semantic nuances inherent in enterprise-grade SQL dialects.

Key Evaluation Metrics

ESQ-Bench focuses on two primary challenges:

• Dialect Generalization: This measures a model's ability to translate natural language into SQL across different database systems (e.g., PostgreSQL, MySQL, SQL Server) without requiring system-specific fine-tuning. It tests whether a model has learned the underlying logic of SQL or if it is merely memorizing patterns from a single, dominant dialect.
• Silent Semantic Divergence: This addresses the dangerous phenomenon where a generated SQL query is syntactically correct but semantically incorrect—meaning it executes without error but returns data that does not accurately reflect the user's intent. By identifying these 'silent' failures, the benchmark provides a more rigorous assessment of model reliability in production environments where incorrect data retrieval can have significant business consequences.

Multi-Tier Enterprise Oracle Approach

By utilizing a multi-tier oracle system, ESQ-Bench provides a more granular analysis of model performance. This approach allows researchers to pinpoint exactly where a model fails—whether it is in understanding the natural language prompt, mapping entities to the database schema, or constructing the correct SQL syntax for a specific dialect. This structured evaluation helps developers move beyond simple accuracy scores to understand the specific architectural weaknesses of their AI agents.