Public articles linked to the same research event.
arXiv The work presents FLINT, a domain-specialized Text-to-SQL system for production financial databases that resolves natural-language concepts into question-specific reference table constraints via a lookup agent, retrieves structurally similar query templates from a compact expert-authored bank using embedding-based retrieval, and prunes a large table schema by traversing foreign-key chains; evaluated on two datasets totaling 359 questions over production financial schemas, it outperforms various state-of-the-art baselines using the same LLM and is deployed in production as part of a financial data retrieval service.
The work presents FLINT, a domain-specialized Text-to-SQL system for production financial databases that resolves natural-language concepts into question-specific reference table constraints via a lookup agent, retrieves structurally similar query templates from a compact expert-authored bank using embedding-based retrieval, and prunes a large table schema by traversing foreign-key chains; evaluated on two datasets totaling 359 questions over production financial schemas, it outperforms various state-of-the-art baselines using the same LLM and is deployed in production as part of a financial data retrieval service.
The work presents FLINT, a domain-specialized Text-to-SQL system for production financial databases that resolves natural-language concepts into question-specific reference table constraints via a lookup agent, retrieves structurally similar query templates from a compact expert-authored bank using embedding-based retrieval, and prunes a large table schema by traversing foreign-key chains; evaluated on two datasets totaling 359 questions over production financial schemas, it outperforms various state-of-the-art baselines using the same LLM and is deployed in production as part of a financial data retrieval service.
The work presents FLINT, a domain-specialized Text-to-SQL system for production financial databases that resolves natural-language concepts into question-specific reference table constraints via a lookup agent, retrieves structurally similar query templates from a compact expert-authored bank using embedding-based retrieval, and prunes a large table schema by traversing foreign-key chains; evaluated on two datasets totaling 359 questions over production financial schemas, it outperforms various state-of-the-art baselines using the same LLM and is deployed in production as part of a financial data retrieval service.