Conventional supervised approaches for text-to-SQL parsing often require
large amounts of annotated data, which is costly to obtain in practice.
Recently, in-context learning with large language models (LLMs) has caught
increasing attention due to its superior few-shot performance in a wide range
of tasks. However, most attempts to use in-context learning for text-to-SQL
parsing still lag behind supervised methods. We hypothesize that the
under-performance is because text-to-SQL parsing requires complex, multi-step
reasoning. In this paper, we systematically study how to enhance the reasoning
ability of LLMs for text-to-SQL parsing through chain-of-thought (CoT) style
promptings including CoT prompting and Least-to-Most prompting. Our experiments
demonstrate that iterative prompting as in Least-to-Most prompting may be
unnecessary for text-to-SQL parsing and directly applying existing CoT style
prompting methods leads to error propagation issues. By improving multi-step
reasoning while avoiding much detailed information in the reasoning steps which
may lead to error propagation, our new method outperforms existing ones by 2.4
point absolute gains on the Spider development set.

本文研究如何通过链式思维提示增强 LLM 的文本到 SQL 解析的推理能力，实验结果表明，通过避免错误传播的详细信息，新方法在 Spider 开发集上的表现比现有方法提高了 2.4 个绝对值。