mlsanityData to 14 Oct 2026

Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities

arXiv 2505.23838 ↗First posted 28 May 2025cs.CL cs.IRcited by 4

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Abstract (arXiv)

Converting natural language (NL) questions into SQL queries, referred to as Text-to-SQL, has emerged as a pivotal technology for facilitating access to relational databases, especially for users without SQL knowledge. Recent progress in large language models (LLMs) has markedly propelled the field of natural language processing (NLP), opening new avenues to improve text-to-SQL systems. This study presents a systematic review of LLM-based text-to-SQL, focusing on four key aspects: (1) an analysis of the research trends in LLM-based text-to-SQL; (2) an in-depth analysis of existing LLM-based text-to-SQL techniques from diverse perspectives; (3) summarization of existing text-to-SQL datasets and evaluation metrics; and (4) discussion on potential obstacles and avenues for future exploration in this domain. This survey seeks to furnish researchers with an in-depth understanding of LLM-based text-to-SQL, sparking new innovations and advancements in this field.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 136 of its 168 references that are in the map. First posted on arXiv 28 May 2025.

Builds on

136 of 168 references.

References · 136

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  4. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
  5. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
  6. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  7. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,407
  8. 2023A Survey on Evaluation of Large Language Modelscited by 1,242
  9. 2024DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligencecited by 1,181
  10. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  11. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,122
  12. 2023Teaching Large Language Models to Self-Debugcited by 988
38 more
  1. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 688
  2. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 687
  3. 2023LLM-Pruner: On the Structural Pruning of Large Language Modelscited by 650
  4. 2022Towards Reasoning in Large Language Models: A Surveycited by 596
  5. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 477
  6. 2023Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyondcited by 411
  7. 2020TaBERT: Pretraining for Joint Understanding of Textual and Tabular Datacited by 398
  8. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 385
  9. 2023Explainability for Large Language Models: A Surveycited by 333
  10. 2024On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Surveycited by 244
  11. 2023LEVER: Learning to Verify Language-to-Code Generation with Executioncited by 239
  12. 2022Binding Language Models in Symbolic Languagescited by 228
  13. 2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 195
  14. 2024CodeS: Towards Building Open-source Language Models for Text-to-SQLcited by 187
  15. 2019CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databasescited by 185
  16. 2020GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsingcited by 171
  17. 2024The Effect of Sampling Temperature on Problem Solving in Large Language Modelscited by 170
  18. 2024CHESS: Contextual Harnessing for Efficient SQL Synthesiscited by 169
  19. 2023C3: Zero-shot Text-to-SQL with ChatGPTcited by 164
  20. 2023MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQLcited by 164
  21. 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 163
  22. 2020Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?cited by 147
  23. 2019SParC: Cross-Domain Semantic Parsing in Contextcited by 144
  24. 2022On the Effectiveness of Parameter-Efficient Fine-Tuningcited by 141
  25. 2023ChatDB: Augmenting LLMs with Databases as Their Symbolic Memorycited by 140
  26. 2024CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQLcited by 138
  27. 2022A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Modelscited by 129
  28. 2020Semantic Evaluation for Text-to-SQL with Distilled Test Suitescited by 126
  29. 2020Structure-Grounded Pretraining for Text-to-SQLcited by 126
  30. 2023Accelerating Transformer Inference for Translation via Parallel Decodingcited by 124
  31. 2023A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capabilitycited by 119
  32. 2021Towards Robustness of Text-to-SQL Models against Synonym Substitutioncited by 110
  33. 2021KaggleDBQA: Realistic Evaluation of Text-to-SQL Parserscited by 106
  34. 2024MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generationcited by 105
  35. 2022Evaluating the Text-to-SQL Capabilities of Large Language Modelscited by 101
  36. 2021Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalizationcited by 92
  37. 2024The Dawn of Natural Language to SQL: Are We Fully Ready?cited by 79
  38. 2019Text-to-SQL Generation for Question Answering on Electronic Medical Recordscited by 77

50 most cited of 136 All 136 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Text2Cypher Across Languages: Evaluating and Finetuning LLMscited by 3
  2. 2025MaskSQL: Safeguarding Privacy for LLM-Based Text-to-SQL via Abstractioncited by 3
  3. 2025VizGen: Data Exploration and Visualization from Natural Language via a Multi-Agent AI Architecturecited by 0
  4. 2026Incremental Multilingual Text2Cypher with Adapter Combinationcited by 0