FINCH: Financial Intelligence using Natural language for Contextualized SQL Handling
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916985517899776 |
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| author | Singh, Avinash Kumar Sarmah, Bhaskarjit Pasquali, Stefano |
| author_facet | Singh, Avinash Kumar Sarmah, Bhaskarjit Pasquali, Stefano |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01887 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FINCH: Financial Intelligence using Natural language for Contextualized SQL Handling Singh, Avinash Kumar Sarmah, Bhaskarjit Pasquali, Stefano Computational Finance Artificial Intelligence 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. |
| title | FINCH: Financial Intelligence using Natural language for Contextualized SQL Handling |
| topic | Computational Finance Artificial Intelligence |
| url | https://arxiv.org/abs/2510.01887 |