UniQL: Towards Dialect-Universal Benchmarking for Text-to-SQL

UniQL introduces a new benchmark for evaluating text-to-SQL models across 16 different SQL dialects, addressing a critical gap where existing benchmarks focus primarily on SQLite. The study reveals that current large language models struggle with cross-dialect generalization, performing inconsistently across different database systems despite success on SQLite.

UniQL addresses a fundamental limitation in AI model evaluation for database query generation. While existing text-to-SQL benchmarks predominantly rely on SQLite, production systems use diverse SQL dialects with varying syntax, functions, and semantics. This creates a false sense of model capability—systems that perform well on SQLite may fail when applied to PostgreSQL, MySQL, or enterprise databases like Oracle. The benchmark's construction is rigorous, involving 1,534 aligned natural language questions translated into 24,544 dialect-specific queries across 16 different systems, all verified through execution and human validation. This represents substantial methodological progress in benchmark design.

The research reveals performance gaps that have significant implications for AI development. Current LLMs exhibit substantial variance across database systems, indicating they lack true dialect-universal understanding. Transfer learning from SQLite success doesn't reliably translate to other platforms, suggesting models memorize SQLite patterns rather than learning generalizable SQL principles. This finding matters because real-world applications demand reliability across heterogeneous database environments. Organizations deploying text-to-SQL systems based on narrow benchmarks may encounter unexpected failures in production.

For the AI research community, UniQL establishes a new evaluation standard that will influence future model development and training strategies. Developers will need to incorporate multi-dialect training data and dialect-aware architectures. The open-source release of code and data democratizes access to this evaluation framework, potentially accelerating progress toward more robust solutions. Future benchmarking efforts will likely follow UniQL's hybrid pipeline methodology, establishing it as a reference standard.

• →UniQL benchmark covers 16 SQL dialects with 24,544 aligned queries, revealing models cannot reliably generalize across database systems
• →Current LLMs show significant performance variation across different SQL dialects despite strong SQLite performance
• →Real-world database diversity demands dialect-aware training methods that existing models fundamentally lack
• →The benchmark's execution-guided verification and human validation ensure practical applicability beyond theoretical correctness
• →Open-source release enables industry-wide adoption of cross-dialect evaluation standards