mlsanityData to 14 Oct 2026

Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL

arXiv 2406.08426 ↗First posted 12 Jun 2024cs.CL cs.AI cs.DBcited by 120

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Cited by 24 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Generating accurate SQL from users' natural language questions (text-to-SQL) remains a long-standing challenge due to the complexities involved in user question understanding, database schema comprehension, and SQL generation. Traditional text-to-SQL systems, which combine human engineering and deep neural networks, have made significant progress. Subsequently, pre-trained language models (PLMs) have been developed for text-to-SQL tasks, achieving promising results. However, as modern databases and user questions grow more complex, PLMs with a limited parameter size often produce incorrect SQL. This necessitates more sophisticated and tailored optimization methods, which restricts the application of PLM-based systems. Recently, large language models (LLMs) have shown significant capabilities in natural language understanding as model scale increases. Thus, integrating LLM-based solutions can bring unique opportunities, improvements, and solutions to text-to-SQL research. In this survey, we provide a comprehensive review of existing LLM-based text-to-SQL studies. Specifically, we offer a brief overview of the technical challenges and evolutionary process of text-to-SQL. Next, we introduce the datasets and metrics designed to evaluate text-to-SQL systems. Subsequently, we present a systematic analysis of recent advances in LLM-based text-to-SQL. Finally, we make a summarization and discuss the remaining challenges in this field and suggest expectations for future research directions. All the related resources of LLM-based, including research papers, benchmarks, and open-source projects, are collected for the community in our repository: https://github.com/DEEP-PolyU/Awesome-LLM-based-Text2SQL.

Key facts. Cited by 120 papers in the map (data through 14 Oct 2026). Builds on 135 of its 193 references that are in the map. First posted on arXiv 12 Jun 2024.

Builds on

135 of 193 references.

References · 135

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50 most cited of 135 All 135 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 15; 2025: 58; 2026: 47.

120 papers.

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  1. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 88
  2. 2024A Survey on Employing Large Language Models for Text-to-SQL Taskscited by 67
  3. 2024The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Modelscited by 67
  4. 2024A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?cited by 56
  5. 2025LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corporacited by 49
  6. 2025When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generationcited by 48
  7. 2025Table-R1: Region-based Reinforcement Learning for Table Understandingcited by 37
  8. 2024Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacycited by 36
  9. 2024Interpretable LLM-based Table Question Answeringcited by 18
  10. 2025AutoLink: Autonomous Schema Exploration and Expansion for Scalable Schema Linking in Text-to-SQL at Scalecited by 17
  11. 2024From Natural Language to SQL: Review of LLM-based Text-to-SQL Systemscited by 16
  12. 2025MARS-SQL: A multi-agent reinforcement learning framework for Text-to-SQLcited by 15
38 more
  1. 2024FLEX: Expert-level False-Less EXecution Metric for Reliable Text-to-SQL Benchmarkcited by 11
  2. 2024Accurate and Regret-aware Numerical Problem Solver for Tabular Question Answeringcited by 10
  3. 2024A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challengescited by 10
  4. 2025SLM-SQL: An Exploration of Small Language Models for Text-to-SQLcited by 10
  5. 2025MTIR-SQL: Multi-turn Tool-Integrated Reasoning Reinforcement Learning for Text-to-SQLcited by 10
  6. 2025From Text to Trust: Empowering AI-assisted Decision Making with Adaptive LLM-powered Analysiscited by 9
  7. 2025Knapsack Optimization-based Schema Linking for LLM-based Text-to-SQL Generationcited by 9
  8. 2025Sparks of Tabular Reasoning via Text2SQL Reinforcement Learningcited by 9
  9. 2025Multi-agent systems for chemical engineering: A review and perspectivecited by 9
  10. 2024Unconstrained Model Merging for Enhanced LLM Reasoningcited by 7
  11. 2025LogicCat: A Chain-of-Thought Text-to-SQL Benchmark for Complex Reasoningcited by 7
  12. 2026Arming Data Agents with Tribal Knowledgecited by 7
  13. 2024DAC: Decomposed Automation Correction for Text-to-SQLcited by 6
  14. 2025Are Your LLM-based Text-to-SQL Models Secure? Exploring SQL Injection via Backdoor Attackscited by 6
  15. 2024Towards Optimizing SQL Generation via LLM Routingcited by 5
  16. 2025Query and Conquer: Execution-Guided SQL Generationcited by 5
  17. 2025SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQLcited by 4
  18. 2025GenEdit: Compounding Operators and Continuous Improvement to Tackle Text-to-SQL in the Enterprisecited by 4
  19. 2025JOLT-SQL: Joint Loss Tuning of Text-to-SQL with Confusion-aware Noisy Schema Samplingcited by 4
  20. 2025SQUiD: Synthesizing Relational Databases from Unstructured Textcited by 4
  21. 2025SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generationcited by 4
  22. 2025RASL: Retrieval Augmented Schema Linking for Massive Database Text-to-SQLcited by 4
  23. 2025Rethinking Schema Linking: A Context-Aware Bidirectional Retrieval Approach for Text-to-SQLcited by 4
  24. 2026FlexSQL: Flexible Exploration and Execution Make Better Text-to-SQL Agentscited by 4
  25. 2025Towards a Barrier-free GeoQA Portal: Natural Language Interaction with Geospatial Data Using Multi-Agent LLMs and Semantic Searchcited by 3
  26. 2025Abacus-SQL: A Text-to-SQL System Empowering Cross-Domain and Open-Domain Database Retrievalcited by 3
  27. 2025Pi-SQL: Enhancing Text-to-SQL with Fine-Grained Guidance from Pivot Programming Languagescited by 3
  28. 2025LLM Agents Are the Antidote to Walled Gardenscited by 3
  29. 2025LoSemB: Logic-Guided Semantic Bridging for Inductive Tool Retrievalcited by 3
  30. 2026ReFuGe: Feature Generation for Prediction Tasks on Relational Databases with LLM Agentscited by 3
  31. 2026Semantic Layers for Reliable LLM-Powered Data Analytics: A Paired Benchmark of Accuracy and Hallucination Across Three Frontier Modelscited by 3
  32. 2024Multi-Turn Interactions for Text-to-SQL with Large Language Modelscited by 2
  33. 2025Querying Databases with Function Callingcited by 2
  34. 2025Macro Graph of Experts for Billion-Scale Multi-Task Recommendationcited by 2
  35. 2025LLM-based Question-Answer Framework for Sensor-driven HVAC System Interactioncited by 2
  36. 2025Exploring Database Normalization Effects on SQL Generationcited by 2
  37. 2025DataSage: Multi-agent Collaboration for Insight Discovery with External Knowledge Retrieval, Multi-role Debating, and Multi-path Reasoningcited by 2
  38. 2026LLM-Based SQL Generation: Prompting, Self-Refinement, and Adaptive Weighted Majority Votingcited by 2

50 most cited of 120 All 120 (JSON)