AI RESEARCH
EGREFINE: An Execution-Grounded Optimization Framework for Text-to-SQL Schema Refinement
arXiv CS.CL
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ArXi:2605.00628v1 Announce Type: cross Text-to-SQL enables non-expert users to query databases in natural language, yet real-world schemas often suffer from ambiguous, abbreviated, or inconsistent naming conventions that degrade model accuracy. Existing approaches treat schemas as fixed and address errors downstream. In this paper, we frame schema refinement as a constrained optimization problem: find a renaming function that maximizes downstream Text-to-SQL execution accuracy while preserving query equivalence through database views.