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Text-to-SQL models can now achieve significantly higher accuracy by grouping and ranking SQL candidates based on execution results, then strategically resampling when the initial pool is lacking.
LLMs predict test outputs far more reliably when they can vote between running buggy code and "reasoning" over pseudocode.