mlsanityData to 7 Oct 2026

Selective Demonstrations for Cross-domain Text-to-SQL

arXiv 2310.06302 ↗First posted 10 Oct 2023cs.CLcited by 21

Not on the map. Why?

Abstract (arXiv)

Large language models (LLMs) with in-context learning have demonstrated impressive generalization capabilities in the cross-domain text-to-SQL task, without the use of in-domain annotations. However, incorporating in-domain demonstration examples has been found to greatly enhance LLMs' performance. In this paper, we delve into the key factors within in-domain examples that contribute to the improvement and explore whether we can harness these benefits without relying on in-domain annotations. Based on our findings, we propose a demonstration selection framework ODIS which utilizes both out-of-domain examples and synthetically generated in-domain examples to construct demonstrations. By retrieving demonstrations from hybrid sources, ODIS leverages the advantages of both, showcasing its effectiveness compared to baseline methods that rely on a single data source. Furthermore, ODIS outperforms state-of-the-art approaches on two cross-domain text-to-SQL datasets, with improvements of 1.1 and 11.8 points in execution accuracy, respectively.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 44 of its 48 references that are in the map. First posted on arXiv 10 Oct 2023.

Builds on

44 of 48 references.

References · 44

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  5. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  6. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  7. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  8. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  9. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,413
  10. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
  11. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  12. 2023Teaching Large Language Models to Self-Debugcited by 963
32 more
  1. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 667
  2. 2021Learning To Retrieve Prompts for In-Context Learningcited by 641
  3. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 465
  4. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 402
  5. 2022Selective Annotation Makes Language Models Better Few-Shot Learnerscited by 242
  6. 2021TAPEX: Table Pre-training via Learning a Neural SQL Executorcited by 233
  7. 2023LEVER: Learning to Verify Language-to-Code Generation with Executioncited by 228
  8. 2022Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matterscited by 221
  9. 2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 191
  10. 2020GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsingcited by 171
  11. 2022Synchromesh: Reliable code generation from pre-trained language modelscited by 156
  12. 2022Diverse Demonstrations Improve In-context Compositional Generalizationcited by 136
  13. 2018Improving Text-to-SQL Evaluation Methodologycited by 133
  14. 2020Semantic Evaluation for Text-to-SQL with Distilled Test Suitescited by 123
  15. 2023A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capabilitycited by 118
  16. 2021KaggleDBQA: Realistic Evaluation of Text-to-SQL Parserscited by 105
  17. 2022Evaluating the Text-to-SQL Capabilities of Large Language Modelscited by 101
  18. 2020SmBoP: Semi-autoregressive Bottom-up Semantic Parsingcited by 97
  19. 2020Grounded Adaptation for Zero-shot Executable Semantic Parsingcited by 76
  20. 2023Exploring Chain-of-Thought Style Prompting for Text-to-SQLcited by 69
  21. 2022Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Datacited by 61
  22. 2023How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settingscited by 57
  23. 2023Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustnesscited by 50
  24. 2023Skill-Based Few-Shot Selection for In-Context Learningcited by 46
  25. 2023Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategiescited by 44
  26. 2021Learning to Synthesize Data for Semantic Parsingcited by 41
  27. 2021Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsingcited by 32
  28. 2022XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsingcited by 32
  29. 2018Question Generation from SQL Queries Improves Neural Semantic Parsingcited by 29
  30. 2022Importance of Synthesizing High-quality Data for Text-to-SQL Parsingcited by 27
  31. 2023UNITE: A Unified Benchmark for Text-to-SQL Evaluationcited by 14
  32. 2021Hierarchical Neural Data Synthesis for Semantic Parsingcited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 13; 2025: 6; 2026: 1.

21 papers.

Sort:

By area:

  1. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 352
  2. 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 118
  3. 2024A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?cited by 54
  4. 2024SQLBench: A Comprehensive Evaluation for Text-to-SQL Capabilities of Large Language Modelscited by 32
  5. 2024Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigmcited by 31
  6. 2024A Survey of Table Reasoning with Large Language Modelscited by 29
  7. 2025Reliable Text-to-SQL with Adaptive Abstentioncited by 19
  8. 2024You Only Read Once (YORO): Learning to Internalize Database Knowledge for Text-to-SQLcited by 11
  9. 2024Solid-SQL: Enhanced Schema-linking based In-context Learning for Robust Text-to-SQLcited by 11
  10. 2024Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQLcited by 10
  11. 2023DBCopilot: Natural Language Querying over Massive Databases via Schema Routingcited by 9
  12. 2024Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health recordscited by 8
9 more
  1. 2024DAC: Decomposed Automation Correction for Text-to-SQLcited by 6
  2. 2024Leveraging Prior Experience: An Expandable Auxiliary Knowledge Base for Text-to-SQLcited by 5
  3. 2025Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generationcited by 4
  4. 2025Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunitiescited by 4
  5. 2025RubikSQL: Lifelong Learning Agentic Knowledge Base as an Industrial NL2SQL Systemcited by 4
  6. 2024In-Context Transfer Learning: Demonstration Synthesis by Transferring Similar Taskscited by 3
  7. 2025Learning-to-Context Slope: Evaluating In-Context Learning Effectiveness Beyond Performance Illusionscited by 1
  8. 2025ORANGE: An Online Reflection ANd GEneration framework with Domain Knowledge for Text-to-SQLcited by 1
  9. 2026Benchmarking Text-to-SQL under Role-Based Access Controlcited by 0