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

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arxiv 2406.10593 v3 pith:HRE3BHO6 submitted 2024-06-15 cs.AI cs.DBcs.IR

Yinggang Sun , Ziming Guo , Haining Yu , Chuanyi Liu , Xiang Li , Bingxuan Wang , Xiangzhan Yu , Tiancheng Zhao This is my paper · ORCID
classification cs.AIcs.DBcs.IR
keywords multi-turntaskstext-to-sqlqda-sqlquestionshandlellmsmodels
verification ladder T0 review T1 audit T2 compute T3 formal
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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

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types

    cs.CL 2024-12 conditional novelty 6.0 of 10

    MMSQL is a multi-turn text-to-SQL benchmark with four question types, and a multi-agent framework with a Question Detector improves LLM performance on it.

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

    cs.CL 2025-05 conditional novelty 4.0 of 10

    A systematic review organizing LLM-based text-to-SQL methods into pre-processing, in-context learning, fine-tuning, and post-processing paradigms, with a catalog of datasets, metrics, challenges, and future directions.

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