mlsanityData to 7 Oct 2026

Query and Conquer: Execution-Guided SQL Generation

arXiv 2503.24364 ↗First posted 31 Mar 2025cs.CLcited by 5

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Abstract (arXiv)

We propose a novel approach for generating complex outputs that significantly improves accuracy in text-to-SQL tasks. Our method leverages execution results to select the most semantically consistent query from multiple candidates, enabling smaller, cost-effective models to surpass computationally intensive reasoning methods such as o1, o3-mini, and DeepSeek R1 while reducing inference cost by as much as 30 times. It integrates effortlessly with existing models, offering a practical and scalable pathway to state-of-the-art SQL generation.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 17 of its 68 references that are in the map. First posted on arXiv 31 Mar 2025.

Builds on

17 of 68 references.

References · 17

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  3. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  4. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  5. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,332
  6. 2024DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligencecited by 1,154
  7. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 667
  8. 2023MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQLcited by 158
  9. 2020Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translationcited by 137
  10. 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 118
  11. 2025Competitive Programming with Large Reasoning Modelscited by 115
  12. 2024MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generationcited by 103
5 more
  1. 2025Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errorscited by 14
  2. 2023Data Ambiguity Strikes Back: How Documentation Improves GPT's Text-to-SQLcited by 11
  3. 2024SQLFixAgent: Towards Semantic-Accurate Text-to-SQL Parsing via Consistency-Enhanced Multi-Agent Collaborationcited by 10
  4. 2021Academic Source Code Plagiarism Detection by Measuring Program Behavioural Similaritycited by 4
  5. 2019Behavioural Equivalence via Modalities for Algebraic Effectscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025ST-Raptor: LLM-Powered Semi-Structured Table Question Answeringcited by 11
  2. 2025GradeSQL: Test-Time Inference with Outcome Reward Models for Text-to-SQL Generation from Large Language Modelscited by 0
  3. 2026Bootstrapping Semantic Layer from Execution for Text-to-SQLcited by 0
  4. 2026DecoSearch: Complexity-Aware Routing and Plan-Level Repair for Text-to-SQLcited by 0
  5. 2026Beam Search, Self-Consistency, and the Limits of Inference-Time Scaling for Grammar-Constrained Text-to-SQL in Small Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Reinforcing Code Generation: Improving Text-to-SQL with Execution-Based Learning
  2. 2026Integrating Reasoning and Generalization in Text-to-SQL via Self-Enhanced Fine-Tuning
  3. 2025Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL
  4. 2025STaR-SQL: Self-Taught Reasoner for Text-to-SQL
  5. 2025RetrySQL: text-to-SQL training with retry data for self-correcting query generation
  6. 2025CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation
  7. 2025BAPPA: Benchmarking Agents, Plans, and Pipelines for Automated Text-to-SQL Generation
  8. 2025RAISE: Reasoning Agent for Interactive SQL Exploration