SQL-CRAFT: Text-to-SQL through Interactive Refinement and Enhanced
  Reasoning
Hanchen Xia Feng Jiang Naihao Deng Cunxiang Wang Guojiang Zhao Rada Mihalcea Yue Zhang
Modern LLMs have become increasingly powerful, but they are still facing
challenges in specialized tasks such as Text-to-SQL. We propose SQL-CRAFT, a
framework to advance LLMs' SQL generation Capabilities through inteRActive
reFinemenT and enhanced reasoning. We leverage an Interactive Correction Loop
(IC-Loop) for LLMs to interact with databases automatically, as well as
Python-enhanced reasoning. We conduct experiments on two Text-to-SQL datasets,
Spider and Bird, with performance improvements of up to 5.7% compared to the
naive prompting method. Moreover, our method surpasses the current
state-of-the-art on the Spider Leaderboard, demonstrating the effectiveness of
our framework.