UniQL: New Benchmark Tests AI Models' Ability to Generate SQL Across Different Database Dialects
Researchers have introduced UniQL, a new benchmark that tests whether AI models can generate correct SQL queries across 16 different database dialects, finding that current models perform poorly outside of SQLite. Most existing text-to-SQL benchmarks focus almost exclusively on SQLite, leaving a major gap in evaluating real-world database compatibility. The findings suggest that strong performance on standard benchmarks does not reliably transfer to other database systems, highlighting a significant limitation in deploying AI-driven database tools.
A team of researchers has released UniQL, a human-verified benchmark designed to evaluate text-to-SQL models across 16 SQL dialects rather than the SQLite-centric datasets that dominate the field. The benchmark aligns 1,534 natural language questions with executable SQL annotations, producing 24,544 dialect-specific queries while keeping schemas, database contents, and query intents consistent across dialects to enable controlled comparisons. The benchmark was constructed through a hybrid pipeline involving database migration, SQL translation, execution-guided verification, iterative rule summarization, and human validation. Experiments on both open-source and closed-source large language models revealed substantial performance variation across database systems, with models that succeed on SQLite frequently failing on other dialects. The authors attribute this to real-world differences in syntax, built-in functions, type systems, and execution semantics across database platforms. These results underscore the need for more dialect-aware training methods and cross-dialect evaluation standards as text-to-SQL systems are increasingly deployed in enterprise environments. Code and data have been made publicly available.
What's missing
The paper does not detail which specific LLMs were tested or report quantitative performance scores in the abstract, making it difficult to assess the magnitude of performance gaps across dialects. It is also unclear how the 16 dialects were selected and whether they represent the most commercially prevalent database systems.
What different sources said
- arXiv cs.AICenter
UniQL: Towards Dialect-Universal Benchmarking for Text-to-SQL
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