用自然语言查询财务报表，支持越南会计标准，响应快且成本低。
FinStat2SQL: A Text2SQL Pipeline for Financial Statement Analysis
- 多智能体架构融合大中小模型，分步完成实体识别、SQL生成和自纠错。
- 7B模型在消费级硬件上达61.33%准确率，响应时间低于4秒。
- 专为越南会计标准（VAS）设计，适合中小企业做财务智能分析。
尽管大型语言模型取得进展，文本转SQL仍面临诸多挑战，尤其在复杂且领域特定的查询中。金融领域中，不同企业与国家的数据库设计和财务报告格式差异巨大，使文本转SQL更加困难。我们提出FinStat2SQL，一个轻量级文本转SQL流程，支持对财务报表的自然语言查询。该系统针对本地标准如VAS，采用多智能体架构，结合大模型与小模型，实现实体提取、SQL生成与自纠错。我们构建了领域专用数据库，并在合成QA数据集上评估模型表现。经微调的7B模型在消费级硬件上达到61.33%准确率，响应时间低于4秒，优于GPT-4o-mini。FinStat2SQL为财务分析提供可扩展、低成本的解决方案，使越南企业也能使用AI驱动的查询能力。
原文摘要 · Abstract (English)
Despite the advancements of large language models, text2sql still faces many challenges, particularly with complex and domain-specific queries. In finance, database designs and financial reporting layouts vary widely between financial entities and countries, making text2sql even more challenging. We present FinStat2SQL, a lightweight text2sql pipeline enabling natural language queries over financial statements. Tailored to local standards like VAS, it combines large and small language models in a multi-agent setup for entity extraction, SQL generation, and self-correction. We build a domain-specific database and evaluate models on a synthetic QA dataset. A fine-tuned 7B model achieves 61.33\% accuracy with sub-4-second response times on consumer hardware, outperforming GPT-4o-mini. FinStat2SQL offers a scalable, cost-efficient solution for financial analysis, making AI-powered querying accessible to Vietnamese enterprises.
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