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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2507.04701 |
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| _version_ | 1866911568067821568 |
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| author | Liu, Yifu Zhu, Yin Gao, Yingqi Luo, Zhiling Li, Xiaoxia Shi, Xiaorong Hong, Yuntao Gao, Jinyang Li, Yu Ding, Bolin Zhou, Jingren |
| author_facet | Liu, Yifu Zhu, Yin Gao, Yingqi Luo, Zhiling Li, Xiaoxia Shi, Xiaorong Hong, Yuntao Gao, Jinyang Li, Yu Ding, Bolin Zhou, Jingren |
| contents | To leverage the advantages of LLM in addressing challenges in the Text-to-SQL task, we present XiYan-SQL, an innovative framework effectively generating and utilizing multiple SQL candidates. It consists of three components: 1) a Schema Filter module filtering and obtaining multiple relevant schemas; 2) a multi-generator ensemble approach generating multiple highquality and diverse SQL queries; 3) a selection model with a candidate reorganization strategy implemented to obtain the optimal SQL query. Specifically, for the multi-generator ensemble, we employ a multi-task fine-tuning strategy to enhance the capabilities of SQL generation models for the intrinsic alignment between SQL and text, and construct multiple generation models with distinct generation styles by fine-tuning across different SQL formats. The experimental results and comprehensive analysis demonstrate the effectiveness and robustness of our framework. Overall, XiYan-SQL achieves a new SOTA performance of 75.63% on the notable BIRD benchmark, surpassing all previous methods. It also attains SOTA performance on the Spider test set with an accuracy of 89.65%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04701 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL Liu, Yifu Zhu, Yin Gao, Yingqi Luo, Zhiling Li, Xiaoxia Shi, Xiaorong Hong, Yuntao Gao, Jinyang Li, Yu Ding, Bolin Zhou, Jingren Computation and Language To leverage the advantages of LLM in addressing challenges in the Text-to-SQL task, we present XiYan-SQL, an innovative framework effectively generating and utilizing multiple SQL candidates. It consists of three components: 1) a Schema Filter module filtering and obtaining multiple relevant schemas; 2) a multi-generator ensemble approach generating multiple highquality and diverse SQL queries; 3) a selection model with a candidate reorganization strategy implemented to obtain the optimal SQL query. Specifically, for the multi-generator ensemble, we employ a multi-task fine-tuning strategy to enhance the capabilities of SQL generation models for the intrinsic alignment between SQL and text, and construct multiple generation models with distinct generation styles by fine-tuning across different SQL formats. The experimental results and comprehensive analysis demonstrate the effectiveness and robustness of our framework. Overall, XiYan-SQL achieves a new SOTA performance of 75.63% on the notable BIRD benchmark, surpassing all previous methods. It also attains SOTA performance on the Spider test set with an accuracy of 89.65%. |
| title | XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.04701 |