Text-to-SQL, which enables natural language interaction with databases, serves as a pivotal method across diverse industries. With new, more powerful large language models (LLMs) emerging every few months, fine-tuning has become incredibly costly, labor-intensive, and error-prone. As an alternative, zero-shot Text-to-SQL, which leverages the growing knowledge and reasoning capabilities encoded in LLMs without task-specific fine-tuning, presents a promising and more challenging direction. To address this challenge, we propose Alpha-SQL, a novel approach that leverages a Monte Carlo Tree Search (MCTS) framework to iteratively infer SQL construction actions based on partial SQL query states. To enhance the framework's reasoning capabilities, we introduce LLM-as-Action-Model to dynamically generate SQL construction actions during the MCTS process, steering the search toward more promising SQL queries. Moreover, Alpha-SQL employs a self-supervised reward function to evaluate the quality of candidate SQL queries, ensuring more accurate and efficient query generation. Experimental results show that Alpha-SQL achieves 69.7% execution accuracy on the BIRD development set, using a 32B open-source LLM without fine-tuning. Alpha-SQL outperforms the best previous zero-shot approach based on GPT-4o by 2.5% on the BIRD development set.

翻译：文本到SQL技术，作为一种实现自然语言与数据库交互的关键方法，在众多行业中发挥着核心作用。随着每隔数月便出现更强大的大语言模型，微调方法已变得极其昂贵、劳动密集且容易出错。作为一种替代方案，零样本文本到SQL技术，利用LLM中日益增长的知识和推理能力，而无需进行任务特定的微调，展现出一个前景广阔且更具挑战性的研究方向。为应对这一挑战，我们提出了Alpha-SQL，这是一种新颖的方法，它利用蒙特卡洛树搜索框架，基于部分SQL查询状态迭代推断SQL构建动作。为了增强框架的推理能力，我们引入了LLM-as-Action-Model，在MCTS过程中动态生成SQL构建动作，引导搜索朝向更具潜力的SQL查询。此外，Alpha-SQL采用了一种自监督奖励函数来评估候选SQL查询的质量，确保生成更准确、更高效的查询。实验结果表明，Alpha-SQL在BIRD开发集上实现了69.7%的执行准确率，且使用的是未经微调的32B开源LLM。Alpha-SQL在BIRD开发集上的表现优于此前基于GPT-4o的最佳零样本方法2.5%。