mlsanityData to 7 Oct 2026

QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQL

arXiv 2406.10593 ↗First posted 15 Jun 2024cs.AI cs.DB cs.IRcited by 5

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Abstract (arXiv)

Fine-tuning large language models (LLMs) for specific domain tasks has achieved great success in Text-to-SQL tasks. However, these fine-tuned models often face challenges with multi-turn Text-to-SQL tasks caused by ambiguous or unanswerable questions. It is desired to enhance LLMs to handle multiple types of questions in multi-turn Text-to-SQL tasks. To address this, we propose a novel data augmentation method, called QDA-SQL, which generates multiple types of multi-turn Q&A pairs using LLMs. In QDA-SQL, we introduce a method incorporating validation and correction mechanisms to handle complex multi-turn Text-to-SQL tasks. Experimental results demonstrate that QDA-SQL enables fine-tuned models to exhibit higher performance on SQL statement accuracy and enhances their ability to handle complex, unanswerable questions in multi-turn Text-to-SQL tasks. The generation script and test set are released at https://github.com/mcxiaoxiao/QDA-SQL

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 17 of its 34 references that are in the map. First posted on arXiv 15 Jun 2024.

Builds on

17 of 34 references.

References · 17

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  3. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  4. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  5. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  6. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 667
  7. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 402
  8. 2019CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databasescited by 180
  9. 2019SParC: Cross-Domain Semantic Parsing in Contextcited by 141
  10. 2024StateFlow: Enhancing LLM Task-Solving through State-Driven Workflowscited by 65
  11. 2023SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)cited by 42
  12. 2021Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsingcited by 32
5 more
  1. 2018Question Generation from SQL Queries Improves Neural Semantic Parsingcited by 29
  2. 2020Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQLcited by 16
  3. 2024TrustSQL: Benchmarking Text-to-SQL Reliability with Penalty-Based Scoringcited by 15
  4. 2022Augmenting Multi-Turn Text-to-SQL Datasets with Self-Playcited by 9
  5. 2023Controllable Data Augmentation for Context-Dependent Text-to-SQLcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 2.

5 papers.

  1. 2024DAC: Decomposed Automation Correction for Text-to-SQLcited by 6
  2. 2025Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunitiescited by 4
  3. 2024Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Typescited by 3
  4. 2026Track-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQLcited by 0
  5. 2026Rose-SQL: Role-State Evolution Guided Structured Reasoning for Multi-Turn Text-to-SQLcited by 0