mlsanityData to 14 Oct 2026

SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data

arXiv 2311.02883 ↗First posted 6 Nov 2023cs.CLcited by 16

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Abstract (arXiv)

Text-to-SQL aims to automate the process of generating SQL queries on a database from natural language text. In this work, we propose "SQLPrompt", tailored to improve the few-shot prompting capabilities of Text-to-SQL for Large Language Models (LLMs). Our methods include innovative prompt design, execution-based consistency decoding strategy which selects the SQL with the most consistent execution outcome among other SQL proposals, and a method that aims to improve performance by diversifying the SQL proposals during consistency selection with different prompt designs ("MixPrompt") and foundation models ("MixLLMs"). We show that SQLPrompt outperforms previous approaches for in-context learning with few labeled data by a large margin, closing the gap with finetuning state-of-the-art with thousands of labeled data.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 11 of its 15 references that are in the map. First posted on arXiv 6 Nov 2023.

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11 of 15 references.

References · 11

  1. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  2. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  3. 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
  4. 2022Emergent Abilities of Large Language Modelscited by 2,512
  5. 2022Mixture-of-Experts with Expert Choice Routingcited by 623
  6. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 411
  7. 2020Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsingcited by 133
  8. 2020Semantic Evaluation for Text-to-SQL with Distilled Test Suitescited by 126
  9. 2023A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capabilitycited by 119
  10. 2022RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQLcited by 85
  11. 2022Towards Understanding Mixture of Experts in Deep Learningcited by 52

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 4; 2026: 5.

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  1. 2025Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQLcited by 59
  2. 2024Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Trainingcited by 57
  3. 2024A Survey of Table Reasoning with Large Language Modelscited by 29
  4. 2024SQL-GEN: Bridging the Dialect Gap for Text-to-SQL Via Synthetic Data And Model Mergingcited by 16
  5. 2023SUQL: Conversational Search over Structured and Unstructured Data with Large Language Modelscited by 13
  6. 2025Can LLMs Narrate Tabular Data? An Evaluation Framework for Natural Language Representations of Text-to-SQL System Outputscited by 7
  7. 2026SQL-Trail: Multi-Turn Reinforcement Learning with Interleaved Feedback for Text-to-SQLcited by 7
  8. 2025Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunitiescited by 4
  9. 2026Ensembling Language Models with Sequential Monte Carlocited by 3
  10. 2024Context-Aware SQL Error Correction Using Few-Shot Learning -- A Novel Approach Based on NLQ, Error, and SQL Similaritycited by 2
  11. 2024Towards Agentic Schema Refinementcited by 2
  12. 2026MDB-Link: Hierarchical Schema Linking for Multi-Database Text-to-SQLcited by 1
4 more
  1. 2023dIR -- Discrete Information Retrieval: Conversational Search over Unstructured (and Structured) Data with Large Language Modelscited by 0
  2. 2025GEMMA-SQL: A Novel Text-to-SQL Model Based on Large Language Modelscited by 0
  3. 2026When Prompts Become Payloads: A Framework for Mitigating SQL Injection Attacks in Large Language Model-Driven Applicationscited by 0
  4. 2026ZAS-SQL: Distilling Rules from Failures for Zero-Shot Text-to-SQLcited by 0