Text-to-SQL Benchmarks Must Address Real-World Data Store Difficulties

Unrealistic Benchmarking Practices

Many existing text-to-SQL benchmarks, while useful for initial development, do not accurately reflect the challenges encountered with real-world data stores. These benchmarks often use clean, well-structured datasets that simplify the task for AI models, leading to inflated performance metrics that do not translate to practical applications.

Challenges of Real-World Data

Real-world databases frequently feature messy schemas, including inconsistent naming conventions, redundant columns, and poorly defined relationships. Data within these systems can also be inconsistent, incomplete, or contain errors. These factors significantly complicate the process for text-to-SQL models attempting to generate accurate SQL queries from natural language.

Domain-Specific Language Barriers

Another significant hurdle is the prevalence of domain-specific terminology in enterprise databases. Users often employ jargon, acronyms, and business-specific terms that are not present in general training datasets. Text-to-SQL models struggle to map these specialized terms to the correct database entities and attributes, leading to incorrect query generation.

Impact on Practical Adoption

The discrepancy between benchmark performance and real-world utility means that text-to-SQL solutions often fail to meet expectations when deployed in actual business environments. This gap hinders the adoption of these technologies, as their perceived capabilities do not align with their actual performance on complex, production-grade data. Addressing these issues is crucial for the broader acceptance and utility of text-to-SQL systems.

Call for Improved Benchmarks

To advance the field, future text-to-SQL benchmarks must incorporate the complexities of real-world data. This includes designing datasets with messy schemas, inconsistent data, and diverse domain-specific terminology. Such benchmarks would provide a more accurate assessment of model capabilities and drive the development of more robust and practically applicable text-to-SQL solutions.