Papers › MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation
MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation
Satya Krishna Gorti, Ilan Gofman, Zhaoyan Liu, Jiapeng Wu, Noël Vouitsis, Guangwei Yu, Jesse C. Cresswell, Rasa Hosseinzadeh
Text-to-SQL generation enables non-experts to interact with databases via natural language. Recent advances rely on large closed-source models like GPT-4 that present challenges in accessibility, privacy, and latency. To address these issues, we focus on developing small, efficient, and open-source text-to-SQL models. We demonstrate the benefits of sampling multiple candidate SQL generations and propose our method, MSc-SQL, to critique them using associated metadata. Our sample critiquing model evaluates multiple outputs simultaneously, achieving state-of-the-art performance compared to other open-source models while remaining competitive with larger models at a much lower cost. Full code can be found at github.com/layer6ai-labs/msc-sql.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text-To-SQL | BIRD (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation) | MSc-SQL | Execution Accuracy % (Dev) | 65.6 | #38 of 41 | Archive leaderboard | report |
| Text-To-SQL | spider | MSc-SQL | Execution Accuracy (Test) | 84.7 | #5 of 20 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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