Yaxun Dai, Wenxuan Xie, Xialie Zhuang, Tianyu Yang, Ziyi Liu, Haiqin Yang, Yiying Yang, Yuhang Zhao, Pingfu Chao, Wenhao Jiang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Recent advances in large language models (LLMs) have led to substantial progress on the Text-to-SQL task.However, existing approaches typically depend on static, preprocessed 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, humancurated 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-Instructbaseline, 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.Our code is publicly available at https://github.com/ Lancelot-Xie/SDE-SQL.