SDE-SQL: Enhancing Text-to-SQL Generation in Large Language Models via Self-Driven Exploration with SQL Probes

AbstractRecent advances in large language models (LLMs) have led to substantial progress on the Text-to-SQL task. However, existing approaches typically depend on static, pre-processed database information supplied at inference time, which restricts the model’s capacity to deeply comprehend the underlying database content. In the absence of dynamic interaction, LLMs are limited to fixed, human-curated context and lack the ability to autonomously query or explore the data. To overcome this limitation, we introduce SDE-SQL, a novel framework that empowers LLMs to perform Self-Driven Exploration of databases during inference. This is achieved through the generation and execution of SQL probes, enabling the model to actively retrieve information and iteratively refine its understanding of the database. Unlike prior methods, SDE-SQL operates in a zero-shot setting, requiring no in-context demonstrations or question-SQL pairs. Evaluated on the BIRD benchmark with Qwen2.5-72B-Instruct, SDE-SQL achieves an 8.02 % relative improvement in execution accuracy over the vanilla Qwen2.5-72B-Instruct baseline, establishing a new state-of-the-art among open-source methods without supervised fine-tuning (SFT) or model ensembling. Furthermore, when combined with SFT, SDE-SQL delivers an additional 0.52 % performance gain.