FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis
This addresses the problem of enabling financial professionals without SQL skills to query databases more effectively, though it is incremental as it adapts existing LLM methods to a specific domain.
The authors tackled the lack of a practical Text-to-SQL benchmark for financial analysis by collecting the BULL dataset and proposing FinSQL, a model-agnostic LLM-based framework, which achieves state-of-the-art performance and up to 36.64% improvement in few-shot cross-database transfer.
Text-to-SQL, which provides zero-code interface for operating relational databases, has gained much attention in financial analysis; because, financial professionals may not well-skilled in SQL programming. However, until now, there is no practical Text-to-SQL benchmark dataset for financial analysis, and existing Text-to-SQL methods have not considered the unique characteristics of databases in financial applications, such as commonly existing wide tables. To address these issues, we collect a practical Text-to-SQL benchmark dataset and propose a model-agnostic Large Language Model (LLMs)-based Text-to-SQL framework for financial analysis. The benchmark dataset, BULL, is collected from the practical financial analysis business of Hundsun Technologies Inc., including databases for fund, stock, and macro economy. Besides, the proposed LLMs-based Text-to-SQL framework, FinSQL, provides a systematic treatment for financial Text-to-SQL from the perspectives of prompt construction, parameter-efficient fine-tuning and output calibration. Extensive experimental results on BULL demonstrate that FinSQL achieves the state-of-the-art Text-to-SQL performance at a small cost; furthermore, FinSQL can bring up to 36.64% performance improvement in scenarios requiring few-shot cross-database model transfer.