首个金融领域文本转SQL数据集，解决专业场景下的精准查询难题。
FINCH: Financial Intelligence using Natural language for Contextualized SQL Handling
- 构建292张金融表、7.5万对自然语言与SQL的专用数据集
- 验证大模型在金融文本转SQL任务中的性能边界
- 设计更贴近业务的评估指标，捕捉传统方法忽略的细节
文本转SQL是自然语言处理中的核心挑战。尽管进展显著，但金融领域因模式复杂、术语专业且出错代价高，仍面临巨大困难。目前缺乏大规模金融专属数据集，制约研究发展。为此，我们推出精心构建的金融数据集FINCH，包含292张表和75,725对自然语言-SQL样本，支持模型微调与严格评估。基于此，我们系统评测了不同规模的语言模型与推理模型在金融文本转SQL任务中的表现，揭示其优劣。最后，提出面向金融场景的评估指标FINCH Score，有效捕捉现有度量标准忽视的关键细节，实现更真实可靠的模型性能评估。
原文摘要 · Abstract (English)
Text-to-SQL, the task of translating natural language questions into SQL queries, has long been a central challenge in NLP. While progress has been significant, applying it to the financial domain remains especially difficult due to complex schema, domain-specific terminology, and high stakes of error. Despite this, there is no dedicated large-scale financial dataset to advance research, creating a critical gap. To address this, we introduce a curated financial dataset (FINCH) comprising 292 tables and 75,725 natural language-SQL pairs, enabling both fine-tuning and rigorous evaluation. Building on this resource, we benchmark reasoning models and language models of varying scales, providing a systematic analysis of their strengths and limitations in financial Text-to-SQL tasks. Finally, we propose a finance-oriented evaluation metric (FINCH Score) that captures nuances overlooked by existing measures, offering a more faithful assessment of model performance.
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