C3: Zero-shot Text-to-SQL with ChatGPT
Xuemei Dong Chao Zhang Yuhang Ge Yuren Mao Yunjun Gao lu Chen Jinshu Lin Dongfang Lou
This paper proposes a ChatGPT-based zero-shot Text-to-SQL method, dubbed C3,
which achieves 82.3\% in terms of execution accuracy on the holdout test set of
Spider and becomes the state-of-the-art zero-shot Text-to-SQL method on the
Spider Challenge. C3 consists of three key components: Clear Prompting (CP),
Calibration with Hints (CH), and Consistent Output (CO), which are
corresponding to the model input, model bias and model output respectively. It
provides a systematic treatment for zero-shot Text-to-SQL. Extensive
experiments have been conducted to verify the effectiveness and efficiency of
our proposed method.