FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial
  Analysis
Chao Zhang Yuren Mao Yijiang Fan Yu Mi Yunjun Gao Lu Chen Dongfang Lou Jinshu Lin
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.