@inproceedings{liu-etal-2025-solid,
    title = "Solid-{SQL}: Enhanced Schema-linking based In-context Learning for Robust Text-to-{SQL}",
    author = "Liu, Geling  and
      Tan, Yunzhi  and
      Zhong, Ruichao  and
      Xie, Yuanzhen  and
      Zhao, Lingchen  and
      Wang, Qian  and
      Hu, Bo  and
      Li, Zang",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2025.coling-main.654/",
    pages = "9793--9803",
    abstract = "Recently, large language models (LLMs) have significantly improved the performance of text-to-SQL systems. Nevertheless, many state-of-the-art (SOTA) approaches have overlooked the critical aspect of system robustness. Our experiments reveal that while LLM-driven methods excel on standard datasets, their accuracy is notably compromised when faced with adversarial perturbations. To address this challenge, we propose a robust text-to-SQL solution, called Solid-SQL, designed to integrate with various LLMs. We focus on the pre-processing stage, training a robust schema-linking model enhanced by LLM-based data augmentation. Additionally, we design a two-round, structural similarity-based example retrieval strategy for in-context learning. Our method achieves SOTA SQL execution accuracy levels of 82.1{\%} and 58.9{\%} on the general Spider and Bird benchmarks, respectively. Furthermore, experimental results show that Solid-SQL delivers an average improvement of 11.6{\%} compared to baselines on the perturbed Spider-Syn, Spider-Realistic, and Dr. Spider benchmarks."
}