STEF: A Production-Native Text-to-SQL Evaluation Framework
A new research paper introduces STEF (Schema-agnostic Text-to-SQL Evaluation Framework), a system designed to evaluate Text-to-SQL (T2SQL) accuracy in production environments without requiring ground-truth queries or database schema. Current benchmarks rely on rule-based SQL matching or schema-dependent semantic parsers, which are rarely available in real-world deployments, leading to silent quality degradation. STEF operates solely on natural language inputs—the user question, an enriched reformulation, and the generated SQL—extracting semantic specifications from both to produce normalized features for evaluation. The framework addresses the fundamental disconnect between existing evaluation methodologies and production constraints, offering a feedback mechanism for continuous improvement. The paper is available on arXiv under ID 2604.28049.
Key facts
- STEF stands for Schema-agnostic Text-to-SQL Evaluation Framework.
- It evaluates T2SQL accuracy without ground-truth queries or database schema.
- Current evaluation methods assume access to structured schema and reference queries.
- Production T2SQL agents lack feedback mechanisms for continuous improvement.
- STEF uses natural language inputs: user question, enriched reformulation, and generated SQL.
- It extracts semantic specifications from both natural language and SQL representations.
- The paper is published on arXiv with ID 2604.28049.
- The framework is designed for production-native evaluation.
Entities
Institutions
- arXiv