What is it about?
This research introduces SelectCraft, a new method to automatically create high-quality, domain-specific datasets for training systems that convert natural language into SQL queries. We focus on the financial domain, generating over 1 million examples and building a specialized language model, BanQL, that outperforms existing models. Our goal is to improve the accuracy and reliability of natural language interfaces in real-world applications.
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Why is it important?
As more software relies on natural language interfaces, it's vital that systems accurately understand and respond to user queries. High-quality training data is essential for this, but it's often missing, especially in specialized fields like finance. Our work helps fill this gap by automatically generating realistic, domain-specific data, leading to more accurate, reliable, and trustworthy AI systems.
Perspectives
Writing this article was both challenging and rewarding, as it allowed me to bring together ideas from language processing, databases, and real-world problem-solving. What started as a technical question, how to generate better training data, grew into something much more impactful. I hope this work shows that even highly technical research like Text-to-SQL has human significance: it's about helping people interact more naturally with the systems that shape their everyday lives. More than anything, I hope this sparks interest in how we can make AI not just smarter, but more relevant to the domains we care about.
Salmane Chafik
Universite Mohammed VI Polytechnique
Read the Original
This page is a summary of: Towards Automating Domain-Specific Data Generation for Text-to-SQL: A Comprehensive Approach, ACM Transactions on Software Engineering and Methodology, June 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3746226.
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