SQLyzr Benchmark Platform for Text-to-SQL Models
A new evaluation and benchmarking platform for Text-to-SQL models, named SQLyzr, has been unveiled by researchers, as outlined in arXiv preprint 2604.21214. This platform overcomes the shortcomings of current benchmarks that depend on single aggregate scores and fail to reflect realistic scenarios. SQLyzr features a variety of evaluation metrics that assess different dimensions of generated queries, facilitates realistic assessments by aligning workloads with actual SQL usage and database scaling, and allows for detailed query classification, error analysis, and workload enhancement. These advancements enable users to effectively diagnose and refine Text-to-SQL models, which have seen substantial improvements thanks to Large Language Models (LLMs) and are increasingly applied in practical settings. The demonstration highlights these functionalities.
Key facts
- SQLyzr is a benchmark and evaluation platform for Text-to-SQL models.
- It was introduced in arXiv preprint 2604.21214.
- Existing benchmarks rely on single aggregate scores and lack realistic settings.
- SQLyzr uses diverse evaluation metrics for multiple aspects of queries.
- It enables realistic evaluation with workload alignment to real-world SQL usage.
- It supports database scaling, query classification, error analysis, and workload augmentation.
- Text-to-SQL models have improved with Large Language Models (LLMs).
- The platform helps users diagnose and improve Text-to-SQL models.
Entities
Institutions
- arXiv