mlsanityData to 14 Oct 2026

How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings

arXiv 2305.11853 ↗First posted 19 May 2023cs.CLcited by 58

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Abstract (arXiv)

Large language models (LLMs) with in-context learning have demonstrated remarkable capability in the text-to-SQL task. Previous research has prompted LLMs with various demonstration-retrieval strategies and intermediate reasoning steps to enhance the performance of LLMs. However, those works often employ varied strategies when constructing the prompt text for text-to-SQL inputs, such as databases and demonstration examples. This leads to a lack of comparability in both the prompt constructions and their primary contributions. Furthermore, selecting an effective prompt construction has emerged as a persistent problem for future research. To address this limitation, we comprehensively investigate the impact of prompt constructions across various settings and provide insights into prompt constructions for future text-to-SQL studies.

Key facts. Cited by 58 papers in the map (data through 14 Oct 2026). Builds on 29 of its 33 references that are in the map. First posted on arXiv 19 May 2023.

Builds on

29 of 33 references.

References · 29

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  5. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  6. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  7. 2023Teaching Large Language Models to Self-Debugcited by 988
  8. 2021Learning To Retrieve Prompts for In-Context Learningcited by 648
  9. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 477
  10. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 424
  11. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 411
  12. 2022Binding Language Models in Symbolic Languagescited by 228
17 more
  1. 2019Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representationcited by 210
  2. 2020GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsingcited by 171
  3. 2022Synchromesh: Reliable code generation from pre-trained language modelscited by 156
  4. 2020Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?cited by 147
  5. 2022Diverse Demonstrations Improve In-context Compositional Generalizationcited by 139
  6. 2018Improving Text-to-SQL Evaluation Methodologycited by 135
  7. 2020Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsingcited by 133
  8. 2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
  9. 2023A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capabilitycited by 119
  10. 2022Evaluating the Text-to-SQL Capabilities of Large Language Modelscited by 101
  11. 2020SmBoP: Semi-autoregressive Bottom-up Semantic Parsingcited by 97
  12. 2023Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsingcited by 93
  13. 2019Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsingcited by 74
  14. 2021Natural SQL: Making SQL Easier to Infer from Natural Language Specificationscited by 68
  15. 2023Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustnesscited by 53
  16. 2021ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parsercited by 35
  17. 2022XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsingcited by 33

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Citing papers per year: 2023: 10; 2024: 32; 2025: 12; 2026: 4.

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  1. 2024Qwen2.5-Coder Technical Reportcited by 1,203
  2. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 385
  3. 2024CodeS: Towards Building Open-source Language Models for Text-to-SQLcited by 187
  4. 2023MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQLcited by 164
  5. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 159
  6. 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 120
  7. 2023Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflowcited by 76
  8. 2024A Survey on Employing Large Language Models for Text-to-SQL Taskscited by 67
  9. 2023ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thoughtcited by 65
  10. 2023Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Codecited by 62
  11. 2023A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challengecited by 55
  12. 2024Synthesizing Text-to-SQL Data from Weak and Strong LLMscited by 54
38 more
  1. 2024TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decompositioncited by 39
  2. 2024Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLMcited by 35
  3. 2024PURPLE: Making a Large Language Model a Better SQL Writercited by 34
  4. 2024Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigmcited by 31
  5. 2023Selective Demonstrations for Cross-domain Text-to-SQLcited by 21
  6. 2025Reliable Text-to-SQL with Adaptive Abstentioncited by 20
  7. 2024TrustSQL: Benchmarking Text-to-SQL Reliability with Penalty-Based Scoringcited by 17
  8. 2024Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruningcited by 16
  9. 2024Aligning Large Language Models to a Domain-specific Graph Database for NL2GQLcited by 14
  10. 2025Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQLcited by 14
  11. 2024SQL-to-Schema Enhances Schema Linking in Text-to-SQLcited by 12
  12. 2024Overview of the EHRSQL 2024 Shared Task on Reliable Text-to-SQL Modeling on Electronic Health Recordscited by 11
  13. 2024Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQLcited by 10
  14. 2025Rethinking Table Instruction Tuningcited by 10
  15. 2023Reboost Large Language Model-based Text-to-SQL, Text-to-Python, and Text-to-Function -- with Real Applications in Traffic Domaincited by 9
  16. 2024Structure Guided Large Language Model for SQL Generationcited by 9
  17. 2025Knowledge Base Construction for Knowledge-Augmented Text-to-SQLcited by 9
  18. 2023Adapt and Decompose: Efficient Generalization of Text-to-SQL via Domain Adapted Least-To-Most Promptingcited by 8
  19. 2024Enhancing Large Language Models with Pseudo- and Multisource- Knowledge Graphs for Open-ended Question Answeringcited by 8
  20. 2024Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health recordscited by 8
  21. 2024SQLfuse: Enhancing Text-to-SQL Performance through Comprehensive LLM Synergycited by 7
  22. 2023Battle of the Large Language Models: Dolly vs LLaMA vs Vicuna vs Guanaco vs Bard vs ChatGPT -- A Text-to-SQL Parsing Comparisoncited by 6
  23. 2024StatBot.Swiss: Bilingual Open Data Exploration in Natural Languagecited by 6
  24. 2024Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minoritiescited by 5
  25. 2024Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Modelscited by 5
  26. 2024Leveraging Prior Experience: An Expandable Auxiliary Knowledge Base for Text-to-SQLcited by 5
  27. 2024SelECT-SQL: Self-correcting ensemble Chain-of-Thought for Text-to-SQLcited by 4
  28. 2025Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generationcited by 4
  29. 2025DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graphcited by 4
  30. 2025Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunitiescited by 4
  31. 2025SpotIt: Evaluating Text-to-SQL Evaluation with Formal Verificationcited by 4
  32. 2024Improving Factual Accuracy of Neural Table-to-Text Output by Addressing Input Problems in ToTTocited by 3
  33. 2024KU-DMIS at EHRSQL 2024:Generating SQL query via question templatization in EHRcited by 3
  34. 2024AnnotatedTables: A Large Tabular Dataset with Language Model Annotationscited by 3
  35. 2024Fact Finder -- Enhancing Domain Expertise of Large Language Models by Incorporating Knowledge Graphscited by 3
  36. 2024Filling Memory Gaps: Enhancing Continual Semantic Parsing via SQL Syntax Variance-Guided LLMs without Real Data Replaycited by 3
  37. 2025What Really Matters for Table LLMs? A Meta-Evaluation of Model and Data Effectscited by 2
  38. 2025Are Human Interactions Replicable by Generative Agents? A Case Study on Pronoun Usage in Hierarchical Interactionscited by 2

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  1. 2024RB-SQL: A Retrieval-based LLM Framework for Text-to-SQL
  2. 2023Exploring Chain-of-Thought Style Prompting for Text-to-SQL
  3. 2024CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editions
  4. 2023Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain
  5. 2024PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency
  6. 2023Divide and Prompt: Chain of Thought Prompting for Text-to-SQL
  7. 2025Auto prompt sql: a resource-efficient architecture for text-to-sql translation in constrained environments
  8. 2026Structured Prompt Language: Declarative Context Management for LLMs