mlsanityData to 14 Oct 2026

FinStat2SQL: A Text2SQL Pipeline for Financial Statement Analysis

arXiv 2506.23273 ↗First posted 29 Jun 2025cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 32 of its 55 references that are in the map. First posted on arXiv 29 Jun 2025.

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32 of 55 references.

References · 32

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20 more
  1. 2025Small Language Models are the Future of Agentic AIcited by 270
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