@inproceedings{zhang-etal-2025-high,
    title = "High-Quality Complex Text-to-{SQL} Data Generation through Chain-of-Verification",
    author = "Zhang, Yuchen  and
      Gao, Yuze  and
      Chen, Bin  and
      Li, Wenfeng  and
      Sun, Shuo  and
      Su, Jian",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.findings-ijcnlp.143/",
    pages = "2368--2379",
    ISBN = "979-8-89176-303-6",
    abstract = "Can today{'}s Text-to-SQL benchmarks still stretch modern LLMs? We argue no. Spider1.0 and BIRD, painstakingly hand-built, remain small, costly, and skewed toward middle complex SQL. Meanwhile, LLM-generated corpora are inexpensive but often superficial and fragile suffering from shallow nesting, semantic drift, template fatigue, and insufficient quality check.We address this gap with a Chain-of-Verifications framework that turns a handful of expert-labelled seeds into a large, reliably checked dataset at a fraction of the usual cost. The resulting corpus, AIGT2S, delivers: (1)18k Question{--}SQL pairs across 113 databases, 41{--}77{\%} larger than current English sets; (2)55{\%} queries in the Ultra band of our four-level difficulty taxonomy; (3)87.5{\%} inter-annotator agreement; (4){\ensuremath{\geq}}80{\%} labour and {\ensuremath{\geq}}98{\%} monetary savings versus earlier efforts.Baselines including GPT-4o, Llama3, RESDSQL, and MAC-SQL, achieve at most 56{\%} execution accuracy, indicating substantial room for improvement."
}