FI-NL2PY2SQL: Financial Industry NL2SQL Innovation Model Based on Python and Large Language Model

Xiaozheng Du, Shijing Hu, Feng Zhou (), Cheng Wang and Binh Minh Nguyen
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Xiaozheng Du: School of Computer Science, Fudan University, Shanghai 200438, China
Shijing Hu: School of Computer Science, Fudan University, Shanghai 200438, China
Feng Zhou: School of Artificial Intelligence, Shanghai Normal University Tianhua College, No. 1661 Shengxin North Road, Shanghai 201815, China
Cheng Wang: Business Analysis BU, GienTech Technology Co., Ltd., Shanghai 200232, China
Binh Minh Nguyen: School of Information and Communication Technology, Hanoi University of Science and Technology, No. 1 Dai Co Viet, Hai Ba Trung, Hanoi 100000, Vietnam

Future Internet, 2025, vol. 17, issue 1, 1-24

Abstract: With the rapid development of prominent models, NL2SQL has made many breakthroughs, but customers still hope that the accuracy of NL2SQL can be continuously improved through optimization. The method based on large models has brought revolutionary changes to NL2SQL. This paper innovatively proposes a new NL2SQL method based on a large language model (LLM), which could be adapted to an edge-cloud computing platform. First, natural language is converted into Python language, and then SQL is generated through Python. At the same time, considering the traceability characteristics of financial industry regulatory requirements, this paper uses the open-source big model DeepSeek. After testing on the BIRD dataset, compared with most NL2SQL models based on large language models, EX is at least 2.73% higher than the original method, F1 is at least 3.72 higher than the original method, and VES is 6.34% higher than the original method. Through this innovative algorithm, the accuracy of NL2SQL in the financial industry is greatly improved, which can provide business personnel with a robust database access mode.

Keywords: LLM; NL2SQL; pre-training; prompt; Python (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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