  In-context learning with large language models (LLMs) has recently caught
increasing attention due to its superior few-shot performance on various tasks.
However, its performance on text-to-SQL parsing still has much room for
improvement. In this paper, we hypothesize that a crucial aspect of LLMs to
improve for text-to-SQL parsing is their multi-step reasoning ability. Thus, we
systematically study how to enhance LLMs' reasoning ability through chain of
thought (CoT) style prompting, including the original chain-of-thought
prompting (Wei et al., 2022b) and least-to-most prompting (Zhou et al., 2023).
Our experiments demonstrate that iterative prompting as in Zhou et al. (2023)
may be unnecessary for text-to-SQL parsing, and using detailed reasoning steps
tends to have more error propagation issues. Based on these findings, we
propose a new CoT-style prompting method for text-to-SQL parsing. It brings 5.2
and 6.5 point absolute gains on the Spider development set and the Spider
Realistic set, respectively, compared to the standard prompting method without
reasoning steps; 2.4 and 1.5 point absolute gains, compared to the
least-to-most prompting method.
