mlsanityData to 14 Oct 2026

MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation

arXiv 2410.12916 ↗First posted 16 Oct 2024cs.CLcited by 19

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Abstract (arXiv)

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 https://github.com/layer6ai-labs/msc-sql.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 40 of its 46 references that are in the map. First posted on arXiv 16 Oct 2024.

Builds on

40 of 46 references.

References · 40

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 13; 2026: 5.

19 papers.

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  1. 2024RSL-SQL: Robust Schema Linking in Text-to-SQL Generationcited by 52
  2. 2025CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learningcited by 15
  3. 2025MARS-SQL: A multi-agent reinforcement learning framework for Text-to-SQLcited by 15
  4. 2025SLM-SQL: An Exploration of Small Language Models for Text-to-SQLcited by 10
  5. 2025BASE-SQL: A powerful open source Text-To-SQL baseline approachcited by 9
  6. 2025Are Your LLM-based Text-to-SQL Models Secure? Exploring SQL Injection via Backdoor Attackscited by 6
  7. 2025DB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQLcited by 5
  8. 2025Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunitiescited by 4
  9. 2025SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generationcited by 4
  10. 2025Rethinking Schema Linking: A Context-Aware Bidirectional Retrieval Approach for Text-to-SQLcited by 4
  11. 2025MaskSQL: Safeguarding Privacy for LLM-Based Text-to-SQL via Abstractioncited by 3
  12. 2025Feather-SQL: A Lightweight NL2SQL Framework with Dual-Model Collaboration Paradigm for Small Language Modelscited by 2
7 more
  1. 2025Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Taskcited by 1
  2. 2025UNJOIN: Enhancing Multi-Table Text-to-SQL Generation via Schema Simplificationcited by 0
  3. 2026Monte Carlo Tree Search with Reasoning Path Refinement for Small Language Models in Conversational Text-to-NoSQLcited by 0
  4. 2026ZAS-SQL: Distilling Rules from Failures for Zero-Shot Text-to-SQLcited by 0
  5. 2026Integrating Reasoning and Generalization in Text-to-SQL via Self-Enhanced Fine-Tuningcited by 0
  6. 2026A Gradient Perspective on RLVR Stability and Winner Advantage Policy Optimizationcited by 0
  7. 2026Why, Where, How: Taxonomy-guided Error Grounding for Code Repair in NL2SQLcited by 0