How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings

• URL: http://arxiv.org/abs/2305.11853v3
• Date: Mon, 27 Nov 2023 00:42:07 GMT
• Title: How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings
• Authors: Shuaichen Chang, Eric Fosler-Lussier
• Abstract summary: Large language models (LLMs) with in-context learning have demonstrated remarkable capability in the text-to- task. Previous research has prompted LLMs with various demonstration-retrieval strategies and intermediate reasoning steps to enhance their performance.
• Score: 12.288808992805494
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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