  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.
