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.
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Semantic Parsing | spider | Execution Accuracy (Dev) | 81.8 | C3 + ChatGPT + Zero-Shot |
| Semantic Parsing | spider | Execution Accuracy (Test) | 82.3 | C3 + ChatGPT + Zero-Shot |
| Text-To-SQL | spider | Execution Accuracy (Dev) | 81.8 | C3 + ChatGPT + Zero-Shot |
| Text-To-SQL | spider | Execution Accuracy (Test) | 82.3 | C3 + ChatGPT + Zero-Shot |