AI RESEARCH
R$^3$-SQL: Ranking Reward and Resampling for Text-to-SQL
arXiv CS.CL
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ArXi:2604.25325v1 Announce Type: cross Modern Text-to-SQL systems generate multiple candidate SQL queries and rank them to judge a final prediction. However, existing methods face two limitations. First, they often score functionally equivalent SQL queries inconsistently despite identical execution results. Second, ranking cannot recover when the correct SQL is absent from the candidate pool. We propose R$^3$-SQL, a Text-to-SQL framework that addresses both issues through unified reward for ranking and resampling.