An efficient and effective evaluator for Text2SQL models on unseen and unlabeled data
Author Identifier (ORCID)
Abstract
Recent advances in large language models has strengthened Text2SQL systems that translate natural language questions into database queries. A persistent deployment challenge is to assess a newly trained Text2SQL system on an unseen and unlabeled dataset when no verified answers are available. This situation arises frequently because database content and structure evolve, privacy policies slow manual review, and carefully written SQL labels are costly and time-consuming. Without timely evaluation, organizations cannot approve releases or detect failures early. FusionSQL addresses this gap by working with any Text2SQL models and estimating accuracy without reference labels, allowing teams to measure quality on unseen and unlabeled datasets. It analyzes patterns in the system's own outputs to characterize how the target dataset differs from the material used during training. FusionSQL supports pre-release checks, continuous monitoring of new databases, and detection of quality decline. Experiments across diverse application settings and question types show that FusionSQL closely follows actual accuracy and reliably signals emerging issues. Our code is available at https://github.com/phkhanhtrinh23/FusionSQL.
Keywords
label-free model evaluation, Text2SQL
Document Type
Conference Proceeding
Date of Publication
1-1-2026
Publication Title
2026 IEEE 42nd International Conference on Data Engineering (ICDE)
Publisher
IEEE
School
School of Science
RAS ID
100712
ISBN
[9798331583651]
Copyright
subscription content
First Page
2434
Last Page
2447
Recommended Citation
Pham, T., Nguyen, T. T., Huynh, V., Yin, H., & Nguyen, Q. V. H. (2026). An efficient and effective evaluator for Text2SQL models on unseen and unlabeled data. In 2026 IEEE 42nd International Conference on Data Engineering (ICDE) (pp. 2434–2447). IEEE. https://doi.org/10.1109/ICDE65706.2026.00182