Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2403.09732 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910466758934528 |
|---|---|
| author | Li, Zhishuai Wang, Xiang Zhao, Jingjing Yang, Sun Du, Guoqing Hu, Xiaoru Zhang, Bin Ye, Yuxiao Li, Ziyue Zhao, Rui Mao, Hangyu |
| author_facet | Li, Zhishuai Wang, Xiang Zhao, Jingjing Yang, Sun Du, Guoqing Hu, Xiaoru Zhang, Bin Ye, Yuxiao Li, Ziyue Zhao, Rui Mao, Hangyu |
| contents | Recent advancements in Text-to-SQL (Text2SQL) emphasize stimulating the large language models (LLM) on in-context learning, achieving significant results. Nevertheless, they face challenges when dealing with verbose database information and complex user intentions. This paper presents a two-stage framework to enhance the performance of current LLM-based natural language to SQL systems. We first introduce a novel prompt representation, called reference-enhanced representation, which includes schema information and randomly sampled cell values from tables to instruct LLMs in generating SQL queries. Then, in the first stage, question-SQL pairs are retrieved as few-shot demonstrations, prompting the LLM to generate a preliminary SQL (PreSQL). After that, the mentioned entities in PreSQL are parsed to conduct schema linking, which can significantly compact the useful information. In the second stage, with the linked schema, we simplify the prompt's schema information and instruct the LLM to produce the final SQL. Finally, as the post-refinement module, we propose using cross-consistency across different LLMs rather than self-consistency within a particular LLM. Our methods achieve new SOTA results on the Spider benchmark, with an execution accuracy of 87.6%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_09732 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency Li, Zhishuai Wang, Xiang Zhao, Jingjing Yang, Sun Du, Guoqing Hu, Xiaoru Zhang, Bin Ye, Yuxiao Li, Ziyue Zhao, Rui Mao, Hangyu Computation and Language Artificial Intelligence Recent advancements in Text-to-SQL (Text2SQL) emphasize stimulating the large language models (LLM) on in-context learning, achieving significant results. Nevertheless, they face challenges when dealing with verbose database information and complex user intentions. This paper presents a two-stage framework to enhance the performance of current LLM-based natural language to SQL systems. We first introduce a novel prompt representation, called reference-enhanced representation, which includes schema information and randomly sampled cell values from tables to instruct LLMs in generating SQL queries. Then, in the first stage, question-SQL pairs are retrieved as few-shot demonstrations, prompting the LLM to generate a preliminary SQL (PreSQL). After that, the mentioned entities in PreSQL are parsed to conduct schema linking, which can significantly compact the useful information. In the second stage, with the linked schema, we simplify the prompt's schema information and instruct the LLM to produce the final SQL. Finally, as the post-refinement module, we propose using cross-consistency across different LLMs rather than self-consistency within a particular LLM. Our methods achieve new SOTA results on the Spider benchmark, with an execution accuracy of 87.6%. |
| title | PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2403.09732 |