@inproceedings{
chang2023how,
title={How to Prompt {LLM}s for Text-to-{SQL}: A Study in Zero-shot, Single-domain, and Cross-domain Settings},
author={Shuaichen Chang and Eric Fosler-Lussier},
booktitle={NeurIPS 2023 Second Table Representation Learning Workshop},
year={2023},
url={https://openreview.net/forum?id=5sOZNkkKh3}
}
Large language models (LLMs) with in-context learning have demonstrated remarkable capability in the text-to-SQL task. Previous research has prompted LLMs with various demonstration-retrieval strategies and intermediate reasoning steps to enhance the performance of LLMs. However, those works often employ varied strategies when constructing the prompt text for text-to-SQL inputs, such as databases and demonstration examples. This leads to a lack of comparability in both the prompt constructions and their primary contributions. Furthermore, selecting an effective prompt construction has emerged as a persistent problem for future research. To address this limitation, we comprehensively investigate the impact of prompt constructions across various settings and provide insights into prompt constructions for future text-to-SQL studies.
How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings
Shuaichen Chang Eric Fosler-Lussier