PARROT: A Benchmark for Evaluating LLMs in Cross-System SQL Translation
Authors: name: Wei Zhou; name: Guoliang Li; name: Haoyu Wang; name: Yuxing Han; name: Xufei Wu; name: Fan Wu; name: Xuanhe Zhou
Published: 2025-09-27 14:41:13

Abstract: Large language models (LLMS) have shown increasing effectiveness in
Text-to-SQL tasks. However, another closely related problem, Cross-System SQL
Translation (a.k.a., SQL-to-SQL), which adapts a query written for one database
system (e.g., MySQL) into its equivalent one for another system (e.g.,
ClickHouse), is of great practical importance but remains underexplored.
Existing SQL benchmarks are not well-suited for SQL-to-SQL evaluation, which
(1) focus on a limited set of database systems (often just SQLite) and (2)
cannot capture many system-specific SQL dialects (e.g., customized functions,
data types, and syntax rules). Thus, in this paper, we introduce PARROT, a
Practical And Realistic BenchmaRk for CrOss-System SQL Translation. PARROT
comprises 598 translation pairs from 38 open-source benchmarks and real-world
business services, specifically prepared to challenge system-specific SQL
understanding (e.g., LLMS achieve lower than 38.53% accuracy on average). We
also provide multiple benchmark variants, including PARROT-Diverse with 28,003
translations (for extensive syntax testing) and PARROT-Simple with 5,306
representative samples (for focused stress testing), covering 22
production-grade database systems. To promote future research, we release a
public leaderboard and source code at: https://code4db.github.io/parrot-bench/.