A new evaluation framework generates equivalent schemas from an E/R model and shows that LLMs produce SQL queries with different answers across those schemas for fixed questions and data.
Evaluating the data model robustness of text-to-SQL systems based on real user queries
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.DB 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
EGRefine optimizes column renamings via execution-grounded verification and view materialization to recover Text-to-SQL accuracy lost to schema naming issues while guaranteeing query equivalence.
citing papers explorer
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Same Data, Different Schemas: Robustness of LLM-based Text-to-SQL
A new evaluation framework generates equivalent schemas from an E/R model and shows that LLMs produce SQL queries with different answers across those schemas for fixed questions and data.
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EGREFINE: An Execution-Grounded Optimization Framework for Text-to-SQL Schema Refinement
EGRefine optimizes column renamings via execution-grounded verification and view materialization to recover Text-to-SQL accuracy lost to schema naming issues while guaranteeing query equivalence.