mlsanityData to 7 Oct 2026

SQLfuse: Enhancing Text-to-SQL Performance through Comprehensive LLM Synergy

arXiv 2407.14568 ↗First posted 19 Jul 2024cs.CL cs.AI cs.DBcited by 7

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Abstract (arXiv)

Text-to-SQL conversion is a critical innovation, simplifying the transition from complex SQL to intuitive natural language queries, especially significant given SQL's prevalence in the job market across various roles. The rise of Large Language Models (LLMs) like GPT-3.5 and GPT-4 has greatly advanced this field, offering improved natural language understanding and the ability to generate nuanced SQL statements. However, the potential of open-source LLMs in Text-to-SQL applications remains underexplored, with many frameworks failing to leverage their full capabilities, particularly in handling complex database queries and incorporating feedback for iterative refinement. Addressing these limitations, this paper introduces SQLfuse, a robust system integrating open-source LLMs with a suite of tools to enhance Text-to-SQL translation's accuracy and usability. SQLfuse features four modules: schema mining, schema linking, SQL generation, and a SQL critic module, to not only generate but also continuously enhance SQL query quality. Demonstrated by its leading performance on the Spider Leaderboard and deployment by Ant Group, SQLfuse showcases the practical merits of open-source LLMs in diverse business contexts.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 23 of its 44 references that are in the map. First posted on arXiv 19 Jul 2024.

Builds on

23 of 44 references.

References · 23

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11 more
  1. 2019Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representationcited by 210
  2. 2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 191
  3. 2024CodeS: Towards Building Open-source Language Models for Text-to-SQLcited by 183
  4. 2023C3: Zero-shot Text-to-SQL with ChatGPTcited by 159
  5. 2023A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capabilitycited by 118
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  11. 2017An Encoder-Decoder Framework Translating Natural Language to Database Queriescited by 23

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 1.

7 papers.

  1. 2024A Survey on Employing Large Language Models for Text-to-SQL Taskscited by 67
  2. 2024From Natural Language to SQL: Review of LLM-based Text-to-SQL Systemscited by 15
  3. 2025Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunitiescited by 4
  4. 2025SDE-SQL: Enhancing Text-to-SQL Generation in Large Language Models via Self-Driven Exploration with SQL Probescited by 2
  5. 2025Semantic Captioning: Benchmark Dataset and Graph-Aware Few-Shot In-Context Learning for SQL2Textcited by 1
  6. 2025ORANGE: An Online Reflection ANd GEneration framework with Domain Knowledge for Text-to-SQLcited by 1
  7. 2026Both Ends Count! Just How Good are LLM Agents at "Text-to-Big SQL"?cited by 1