How to Prompt LLMs for Text-to-SQL

In the paper "How to Prompt LLMs for Text-to-SQL," Shuaichen Chang and his co-author investigate the impact of prompt constructions on the performance of large language models (LLMs) in the text-to-SQL task. They focus on zero-shot, single-domain, and cross-domain settings and explore various strategies for prompt construction, evaluating the influence of database schema, content representation, and prompt length on LLMs' effectiveness. The findings emphasize the importance of careful consideration in constructing prompts, highlighting the crucial role of table relationships and content, the effectiveness of in-domain demonstration examples, and the significance of prompt length in cross-domain scenarios.

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