@inproceedings{nguyen-etal-2025-finstat2sql,
    title = "{F}in{S}tat2{SQL}: A {T}ext2{SQL} Pipeline for Financial Statement Analysis",
    author = "Nguyen, Hung Quang  and
      Trinh, Anh Phuong  and
      Mai, Hung Phan Quoc  and
      Trinh, Phong Tuan",
    editor = "Flek, Lucie  and
      Narayan, Shashi  and
      Phương, L{\^e} Hồng  and
      Pei, Jiahuan",
    booktitle = "Proceedings of the 18th International Natural Language Generation Conference",
    month = oct,
    year = "2025",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-luhme/2025.inlg-main.27/",
    pages = "449--464",
    abstract = "Despite recent advances in LLMs, Text2SQL remains challenging for complex, domain-specific queries such as finance, where database designs and reporting standards vary widely. We introduce FinStat2SQL, a lightweight pipeline enabling natural language queries over financial statements, tailored to local standards like VAS. Our multi-agent setup combines large and small language models for entity extraction, SQL generation, and self-correction, and includes a fully automatic pipeline for synthetic data generation. Leveraging this synthetic data, we fine-tuned a 7B model that achieves 61.33{\%} accuracy with sub-4s latency on consumer hardware, outperforming GPT-4o-mini on SQL generation. FinStat2SQL provides a scalable, cost-efficient solution for financial analysis. We made our source code publicly available at: https://github.com/hung20gg/chatbot{\_}financial{\_}statement."
}