<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="nguyen-etal-2025-finstat2sql">
    <titleInfo>
        <title>FinStat2SQL: A Text2SQL Pipeline for Financial Statement Analysis</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Hung</namePart>
        <namePart type="given">Quang</namePart>
        <namePart type="family">Nguyen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Anh</namePart>
        <namePart type="given">Phuong</namePart>
        <namePart type="family">Trinh</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hung</namePart>
        <namePart type="given">Phan</namePart>
        <namePart type="given">Quoc</namePart>
        <namePart type="family">Mai</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Phong</namePart>
        <namePart type="given">Tuan</namePart>
        <namePart type="family">Trinh</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 18th International Natural Language Generation Conference</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Lucie</namePart>
            <namePart type="family">Flek</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Shashi</namePart>
            <namePart type="family">Narayan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lê</namePart>
            <namePart type="given">Hồng</namePart>
            <namePart type="family">Phương</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jiahuan</namePart>
            <namePart type="family">Pei</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Hanoi, Vietnam</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <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.</abstract>
    <identifier type="citekey">nguyen-etal-2025-finstat2sql</identifier>
    <location>
        <url>https://aclanthology.org/2025.inlg-main.27/</url>
    </location>
    <part>
        <date>2025-10</date>
        <extent unit="page">
            <start>449</start>
            <end>464</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T FinStat2SQL: A Text2SQL Pipeline for Financial Statement Analysis
%A Nguyen, Hung Quang
%A Trinh, Anh Phuong
%A Mai, Hung Phan Quoc
%A Trinh, Phong Tuan
%Y Flek, Lucie
%Y Narayan, Shashi
%Y Phương, Lê Hồng
%Y Pei, Jiahuan
%S Proceedings of the 18th International Natural Language Generation Conference
%D 2025
%8 October
%I Association for Computational Linguistics
%C Hanoi, Vietnam
%F nguyen-etal-2025-finstat2sql
%X 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.
%U https://aclanthology.org/2025.inlg-main.27/
%P 449-464

Markdown (Informal)

[FinStat2SQL: A Text2SQL Pipeline for Financial Statement Analysis](https://aclanthology.org/2025.inlg-main.27/) (Nguyen et al., INLG 2025)

• FinStat2SQL: A Text2SQL Pipeline for Financial Statement Analysis (Nguyen et al., INLG 2025)

ACL

• Hung Quang Nguyen, Anh Phuong Trinh, Hung Phan Quoc Mai, and Phong Tuan Trinh. 2025. FinStat2SQL: A Text2SQL Pipeline for Financial Statement Analysis. In Proceedings of the 18th International Natural Language Generation Conference, pages 449–464, Hanoi, Vietnam. Association for Computational Linguistics.