Recently, Large Language Models (LLMs) have been demonstrated to possess
impressive capabilities in a variety of domains and tasks. We investigate the
issue of prompt design in the multi-turn text-to-SQL task and attempt to
enhance the LLMs' reasoning capacity when generating SQL queries. In the
conversational context, the current SQL query can be modified from the
preceding SQL query with only a few operations due to the context dependency.
We introduce our method called CoE-SQL which can prompt LLMs to generate the
SQL query based on the previously generated SQL query with an edition chain. We
also conduct extensive ablation studies to determine the optimal configuration
of our approach. Our approach outperforms different in-context learning
baselines stably and achieves state-of-the-art performances on two benchmarks
SParC and CoSQL using LLMs, which is also competitive to the SOTA fine-tuned
models.

我们通过引入 CoE-SQL 方法，利用上下文依赖关系来改善大型语言模型在多轮文本转 SQL 任务中生成 SQL 查询时的推理能力，并通过大量消融研究确定了最佳配置，从而稳定地优于其他基线模型，同时在 SParC 和 CoSQL 两个基准测试上取得了最新性能，与 SOTA fine-tuned 模型相媲美。