UniQL: New Benchmark Tests AI Models' Ability to Generate SQL Across Different Database Dialects
Researchers introduced UniQL, a benchmark dataset containing 24,544 SQL queries across 16 different SQL dialects, designed to test whether AI models can generalize text-to-SQL capabilities beyond SQLite. Current text-to-SQL benchmarks focus primarily on SQLite, limiting evaluation of real-world applicability where different database systems use different SQL syntax and functions. The findings show that existing large language models struggle with dialect generalization, suggesting the need for more dialect-aware AI methods.
UniQL is a human-verified benchmark that aligns 1,534 natural language questions with executable SQL annotations across 16 SQL dialects, creating 24,544 dialect-specific queries. The benchmark was constructed through a hybrid pipeline combining database migration, SQL translation, execution-guided verification, iterative rule summarization, and human validation. All dialects in UniQL share the same underlying intents, aligned schemas, and database contents, enabling controlled evaluation of how well models transfer knowledge across different SQL systems. Experiments on both open-source and closed-source large language models revealed that current models perform inconsistently across dialects and show limited ability to transfer success from SQLite to other database systems. The researchers argue that this gap highlights a critical need for benchmarks that evaluate cross-dialect generalization and for developing text-to-SQL methods that are more aware of dialect-specific differences.
What different sources said
- arXiv cs.AICenter
UniQL: Towards Dialect-Universal Benchmarking for Text-to-SQL
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