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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.15186 |
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| _version_ | 1866910981239603200 |
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| author | Shi, Liang Tang, Zhengju Zhang, Nan Zhang, Xiaotong Yang, Zhi |
| author_facet | Shi, Liang Tang, Zhengju Zhang, Nan Zhang, Xiaotong Yang, Zhi |
| contents | With the development of the Large Language Models (LLMs), a large range of LLM-based Text-to-SQL(Text2SQL) methods have emerged. This survey provides a comprehensive review of LLM-based Text2SQL studies. We first enumerate classic benchmarks and evaluation metrics. For the two mainstream methods, prompt engineering and finetuning, we introduce a comprehensive taxonomy and offer practical insights into each subcategory. We present an overall analysis of the above methods and various models evaluated on well-known datasets and extract some characteristics. Finally, we discuss the challenges and future directions in this field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_15186 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Survey on Employing Large Language Models for Text-to-SQL Tasks Shi, Liang Tang, Zhengju Zhang, Nan Zhang, Xiaotong Yang, Zhi Computation and Language With the development of the Large Language Models (LLMs), a large range of LLM-based Text-to-SQL(Text2SQL) methods have emerged. This survey provides a comprehensive review of LLM-based Text2SQL studies. We first enumerate classic benchmarks and evaluation metrics. For the two mainstream methods, prompt engineering and finetuning, we introduce a comprehensive taxonomy and offer practical insights into each subcategory. We present an overall analysis of the above methods and various models evaluated on well-known datasets and extract some characteristics. Finally, we discuss the challenges and future directions in this field. |
| title | A Survey on Employing Large Language Models for Text-to-SQL Tasks |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.15186 |