FGCSQL: A Three-Stage Pipeline for Large Language Model-Driven Chinese Text-to-SQL
Year: 2025
Type: article
Abstract: Recent advances in large language models have driven major breakthroughs in Text-to-SQL tasks. However, many challenges hinder the use of SQL parsers for cross-language tasks. In this article, we introduce FGCSQL, a novel three-stage pipeline framework to deal with three challenges: cross-language schema linking, SQL parsing potential of LLM, and error propagation in SQL parsers, in which the framework uniquely incorporates a Filtering Encoder to eliminate irrelevant database schema items, harnessing a pre-trained Generative Large Language Model fine-tuned on a carefully structured dataset for enhanced SQL parsing. Finally, a Correcting Decoder addresses error propagation, culminating in a robust system for semantic parsing tasks. Tested on the CSpider dataset, the FGCSQL showcases a substantial improvement in Exact-set-Match(EM) accuracy and EXecution accuracy(EX) metrics, validating the pipeline’s architecture’s effectiveness in mitigating the challenges typically confronted in Text-to-SQL conversion, especially in cross-lingual contexts. FGCSQL outstrips existing methods in execution precision, indicating the validity of our proposed method.
Source: Electronics
Authors: Guanyu Jiang, Weibin Li, Weibin Li, Chenglong Yu, Zixuan Zhu, Wei LI, Wei LI
Institution: Xidian University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4408656140