Recent advances in text-to-SQL models, which translate natural language questions (NLQs) into executable SQL queries, have made interacting with relational databases more accessible, even for those with limited technical ability. This task has seen significant improvement with the release of multiple English datasets and benchmarks such as WikiSQL, SPIDER, and BIRD, each covering different domains and levels of complexity. Non-English high-resource languages, such as Chinese, Russian, and Arabic, have also benefited from these advances, either through the translation of existing datasets or the creation of new ones. Dialect2SQL , a newly released text-to-SQL dataset, is dedicated to the Moroccan dialect (Darija), which is known for its complexity and distinctiveness compared to other Arabic dialects and Modern Standard Arabic. In this article, we conduct a comprehensive study on text-to-SQL for Darija by conducting several experiments mainly on the Dialect2SQL dataset using different approaches and configurations with two code-based large language models, StarCoder2 and Qwen-2.5-Coder. The experiments reveal the performance gap between models fine-tuned on English data and those fine-tuned on Darija. Additionally, the results illustrate the positive impact of incorporating multi-language datasets during training. In particular, the gap decreases from 10.1% to 6.7% in BLEU, and from 12.5% to 5.7% in TSED.

As software systems increasingly rely on natural language interfaces, ensuring the reliability of these systems is crucial. One critical component is the ability to accurately translate natural language queries into corresponding SQL queries, a field known as Text-to-SQL. However, the scarcity of high-quality, large-scale, and domain-specific Text-to-SQL datasets hinders the development of reliable and robust models. To tackle these challenges, we propose SelectCraft , a novel automatic generation approach designed to create realistic Text-to-SQL datasets tailored to specific domains. Our method leverages existing databases and their structures to generate complex text-SQL pairs that mirror real-world usage scenarios. As a proof of concept, we have successfully generated a substantial financial Text-to-SQL dataset, denominated as BanQies , encompassing over 1 million samples utilizing our proposed approach. Moreover, we introduce BanQL , a new large language model (LLM) based on StarCoder2 , a state-of-the-art code-based LLM, and fine-tuned on our newly created dataset. We evaluate BanQL performance against several state-of-the-art models, demonstrating significant enhancements in accuracy and generalizability, highlighting the advantages of incorporating domain-specific data in Text-to-SQL tasks. We firmly believe that our contributions have the potential to improve the overall reliability of Text-to-SQL software systems. CCS Concepts: • Software and its engineering; • Computing methodologies → Natural languageprocessing; • Information systems → Structured Query Language;