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arXiv · 2303.13547

A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability

Aiwei Liu·Xuming Hu·Lijie Wen·Philip S. Yu
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Abstract

This paper presents the first comprehensive analysis of ChatGPT's Text-to-SQL ability. Given the recent emergence of large-scale conversational language model ChatGPT and its impressive capabilities in both conversational abilities and code generation, we sought to evaluate its Text-to-SQL performance. We conducted experiments on 12 benchmark datasets with different languages, settings, or scenarios, and the results demonstrate that ChatGPT has strong text-to-SQL abilities. Although there is still a gap from the current state-of-the-art (SOTA) model performance, considering that the experiment was conducted in a zero-shot scenario, ChatGPT's performance is still impressive. Notably, in the ADVETA (RPL) scenario, the zero-shot ChatGPT even outperforms the SOTA model that requires fine-tuning on the Spider dataset by 4.1\%, demonstrating its potential for use in practical applications. To support further research in related fields, we have made the data generated by ChatGPT publicly available at https://github.com/THU-BPM/chatgpt-sql.

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Aiwei Liu, Xuming Hu, Lijie Wen, Philip S. Yu. 2023-03-12. A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability. https://arxiv.org/abs/2303.13547

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