@inproceedings{nguyen-etal-2025-sqlong,
    title = "{SQL}ong: Enhanced {NL}2{SQL} for Longer Contexts with {LLM}s",
    author = "Nguyen, Dai Quoc  and
      Hoang, Cong Duy Vu  and
      Vu, Duy Quang  and
      Tangari, Gioacchino  and
      Vu, Thanh  and
      Dharmasiri, Don  and
      Li, Yuan-Fang  and
      Duong, Long",
    editor = "Chang, Shuaichen  and
      Hulsebos, Madelon  and
      Liu, Qian  and
      Chen, Wenhu  and
      Sun, Huan",
    booktitle = "Proceedings of the 4th Table Representation Learning Workshop",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.trl-workshop.5/",
    pages = "47--55",
    ISBN = "979-8-89176-268-8",
    abstract = "Open-weight large language models (LLMs) have significantly advanced performance in the Natural Language to SQL (NL2SQL) task. However, their effectiveness diminishes when dealing with large database schemas, as the context length increases. To address this limitation, we present SQLong, a novel and efficient data augmentation framework designed to enhance LLM performance in long-context scenarios for the NL2SQL task. SQLong generates augmented datasets by extending existing database schemas with additional synthetic CREATE TABLE commands and corresponding data rows, sampled from diverse schemas in the training data. This approach effectively simulates long-context scenarios during finetuning and evaluation. Through experiments on the Spider and BIRD datasets, we demonstrate that LLMs finetuned with SQLong-augmented data significantly outperform those trained on standard datasets. These imply SQLong{'}s practical implementation and its impact on improving NL2SQL capabilities in real-world settings with complex database schemas."
}