TailorSQL: An NL2SQL System Tailored to Your Query Workload

• URL: http://arxiv.org/abs/2505.23039v1
• Date: Thu, 29 May 2025 03:27:22 GMT
• Title: TailorSQL: An NL2SQL System Tailored to Your Query Workload
• Authors: Kapil Vaidya, Jialin Ding, Sebastian Kosak, David Kernert, Chuan Lei, Xiao Qin, Abhinav Tripathy, Ramesh Balan, Balakrishnan Narayanaswamy, Tim Kraska,
• Abstract summary: State-of-the-art NL2 techniques typically perform translation by retrieving database-specific information.<n>We introduce Tailor, a NL2 system that takes advantage of information in the past query workload.<n> Tailor achieves up to 2$times$ improvement in execution accuracy on standardized benchmarks.
• Score: 16.48291142955493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: NL2SQL (natural language to SQL) translates natural language questions into SQL queries, thereby making structured data accessible to non-technical users, serving as the foundation for intelligent data applications. State-of-the-art NL2SQL techniques typically perform translation by retrieving database-specific information, such as the database schema, and invoking a pre-trained large language model (LLM) using the question and retrieved information to generate the SQL query. However, existing NL2SQL techniques miss a key opportunity which is present in real-world settings: NL2SQL is typically applied on existing databases which have already served many SQL queries in the past. The past query workload implicitly contains information which is helpful for accurate NL2SQL translation and is not apparent from the database schema alone, such as common join paths and the semantics of obscurely-named tables and columns. We introduce TailorSQL, a NL2SQL system that takes advantage of information in the past query workload to improve both the accuracy and latency of translating natural language questions into SQL. By specializing to a given workload, TailorSQL achieves up to 2$\times$ improvement in execution accuracy on standardized benchmarks.

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