DCMM-SQL: Automated Data-Centric Pipeline and Multi-Model Collaboration Training for Text-to-SQL Model
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Abstract (arXiv)
Text-to-SQL tasks have gained attractive improvements since the release of ChatGPT. Among them, agent-based frameworks have been widely used in this field. However, the impact of data-centric strategies on text-to-SQL tasks has rarely been explored. In this paper, we systemically design a fully automated data-centric pipeline for text-to-SQL tasks, including adaptive data repair, which can automatically find and fix errors in the training dataset; and error data augmentation, where we specifically diffuse and enhance erroneous data predicted by the initially trained models. Meanwhile, we propose a Multi-Model collaboration training schema, aiming to train multiple models with different augmented data, enabling them to possess distinct capabilities and work together to complement each other, because it has been found that the capability of a single fine-tuned model is very limited. Furthermore, we utilize an ensemble strategy to integrate the capabilities of multiple models to solve a multiple-choice question, aiming to further improve the accuracy of text-to-SQL tasks. The experiment results and ablation study have demonstrated the effectiveness of data-centric pipeline and Multi-Model(MM) interactive iterative strategies, achieving first place in lightweight text-to-SQL models (within 70B).
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 31 references that are in the map. First posted on arXiv 27 Oct 2025.
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References · 25
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13 more
- 2024CodeS: Towards Building Open-source Language Models for Text-to-SQLcited by 183
- 2024CHESS: Contextual Harnessing for Efficient SQL Synthesiscited by 164
- 2023MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQLcited by 158
- 2024CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQLcited by 132
- 2024MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generationcited by 103
- 2025OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scalecited by 90
- 2024Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencentcited by 89
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- 2024The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Modelscited by 67
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- 2024Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigmcited by 31
- 2024Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL Translationcited by 21
- 2024Dubo-SQL: Diverse Retrieval-Augmented Generation and Fine Tuning for Text-to-SQLcited by 12
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