@inproceedings{xie-etal-2026-sde,
    title = "{SDE}-{SQL}: Enhancing Text-to-{SQL} Generation in Large Language Models via Self-Driven Exploration with {SQL} Probes",
    author = "Xie, Wenxuan  and
      Dai, Yaxun  and
      Jiang, Wenhao",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.116/",
    pages = "2507--2525",
    ISBN = "979-8-89176-390-6",
    abstract = "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, 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 $\textbf{SDE-SQL}$, a novel framework that empowers LLMs to perform $\textbf{Self-Driven Exploration}$ of databases during inference. This is achieved through the generation and execution of $\textbf{SQL probes}$, enabling the model to actively retrieve information and iteratively refine its understanding of the database. Unlike prior methods, $\textbf{SDE-SQL}$ operates in a $\textbf{zero-shot}$ setting, requiring no in-context demonstrations or question-SQL pairs. Evaluated on the BIRD benchmark with $\texttt{Qwen2.5-72B-Instruct}$, $\textbf{SDE-SQL}$ achieves an $\textbf{8.02}$ {\%} relative improvement in execution accuracy over the vanilla $\texttt{Qwen2.5-72B-Instruct}$ 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, $\textbf{SDE-SQL}$ delivers an additional $\textbf{0.52}$ {\%} performance gain."
}