Database
Recent advancements in Natural Language Processing (NLP) and Text-to-SQL systems have enabled easier interaction with relational databases. However, most solutions focus on English, leaving Arabic underrepresented. This study addresses that gap by fine-tuning the Llama-3-SQLCoder model to convert Arabic text into correct and executable SQL, enabling non-technical users to work with databases without learning SQL syntax. We enhanced the model using Low-Rank Adaptation (LoRA) and Unsloth, training it on Arabic questions paired with SQL queries from the Northwind database. To support low-resource environments, the model was converted to the GGUF format, reducing computational requirements while preserving performance. Evaluation results showed an execution accuracy of 90.24% and a validity rate of 97.56%, outperforming the zero-shot baseline (44% and 80%). The model also achieved an Exact Match score of 32% and an F1 score of 0.83, compared to 12% and 0.61 for the baseline. These findings demonstrate that LoRA and Unsloth are effective for adapting SQL-specialized models to Arabic. Despite these improvements, the system still struggles with complex nested queries and dialectal variations, indicating areas for future work. Overall, this study contributes to narrowing the gap between Arabic and other languages in Text-to-SQL research and improves database accessibility for non-technical users.
Mohammed Taj, Mohammed Zayed, Abdulwahid Alhetar, Mohammed Rajeh, Mohammed Abbas Al-Sharafi, Basem Abdulrhman Munassar (2026). Enabling Arabic Database Querying via Parameter-Efficient Fine-Tuning of Large Language Models. Sana'a University Journal of Applied Sciences and Technology, 1507-1519. DOI: https://doi.org/10.59628/jast.v4i1.2248
Mohammed Taj, Mohammed Zayed, Abdulwahid Alhetar, Mohammed Rajeh, Mohammed Abbas Al-Sharafi, Basem Abdulrhman Munassar . “Enabling Arabic Database Querying via Parameter-Efficient Fine-Tuning of Large Language Models.” Sana'a University Journal of Applied Sciences and Technology, 2026, pp. 1507-1519. https://doi.org/10.59628/jast.v4i1.2248. Accessed 31 Oct. 2026.
Mohammed Taj, Mohammed Zayed, Abdulwahid Alhetar, Mohammed Rajeh, Mohammed Abbas Al-Sharafi, Basem Abdulrhman Munassar . 2026. “Enabling Arabic Database Querying via Parameter-Efficient Fine-Tuning of Large Language Models.” Sana'a University Journal of Applied Sciences and Technology : 1507-1519. DOI: https://doi.org/10.59628/jast.v4i1.2248
Mohammed Taj, Mohammed Zayed, Abdulwahid Alhetar, Mohammed Rajeh, Mohammed Abbas Al-Sharafi, Basem Abdulrhman Munassar (2026) ‘Enabling Arabic Database Querying via Parameter-Efficient Fine-Tuning of Large Language Models’, Sana'a University Journal of Applied Sciences and Technology, pp. 1507-1519. doi: 10.59628/jast.v4i1.2248
@article{jast2026_art2248,
author = {Mohammed Taj, Mohammed Zayed, Abdulwahid Alhetar, Mohammed Rajeh, Mohammed Abbas Al-Sharafi, Basem Abdulrhman Munassar },
title = {Enabling Arabic Database Querying via Parameter-Efficient Fine-Tuning of Large Language Models},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2026}, month = {January}, pages = {1507-1519}, doi = {10.59628/jast.v4i1.2248},
url = {https://doi.org/10.59628/jast.v4i1.2248},}
Enabling Arabic Database Querying via Parameter-Efficient Fine-Tuning of Large Language Models
Artificial intelligence (AI) significantly enhances our daily lives, driving many service and financial institutions to seek optimal utilization of it. This paper addresses the problem, which is the inability of some institutions, such as banks, to satisfy customers using outdated methods and solely focusing on acquiring new customers instead of prioritizing customer retention that would be more effective and profitable to them. The main object of this paper is to enhance the quality of service in banks by proposing an intelligent model that leverages customer data to improve services and foster customer loyalty. This paper was motivated by the limited research on customer churn in Yemen and the scarcity of simple intelligent models globally. The methodology employed in this paper includes an in-depth analysis of existing literature on customer churn. Finally, this paper provides a comprehensive and concise conclusion based on the findings.
Sultan Yahya AL-SULTAN, Ibrahim Ahmed Al-Baltah (2024). Enhancement Customer Loyalty Via Data Mining Techniques in Yemeni Banks: Review Study. Sana'a University Journal of Applied Sciences and Technology, 348-354. DOI: https://doi.org/10.59628/jast.v2i4.1059
Sultan Yahya AL-SULTAN, Ibrahim Ahmed Al-Baltah . “Enhancement Customer Loyalty Via Data Mining Techniques in Yemeni Banks: Review Study.” Sana'a University Journal of Applied Sciences and Technology, 2024, pp. 348-354. https://doi.org/10.59628/jast.v2i4.1059. Accessed 31 Oct. 2026.
Sultan Yahya AL-SULTAN, Ibrahim Ahmed Al-Baltah . 2024. “Enhancement Customer Loyalty Via Data Mining Techniques in Yemeni Banks: Review Study.” Sana'a University Journal of Applied Sciences and Technology : 348-354. DOI: https://doi.org/10.59628/jast.v2i4.1059
Sultan Yahya AL-SULTAN, Ibrahim Ahmed Al-Baltah (2024) ‘Enhancement Customer Loyalty Via Data Mining Techniques in Yemeni Banks: Review Study’, Sana'a University Journal of Applied Sciences and Technology, pp. 348-354. doi: 10.59628/jast.v2i4.1059
@article{jast2024_art1059,
author = {Sultan Yahya AL-SULTAN, Ibrahim Ahmed Al-Baltah },
title = {Enhancement Customer Loyalty Via Data Mining Techniques in Yemeni Banks: Review Study},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2024}, month = {September}, pages = {348-354}, doi = {10.59628/jast.v2i4.1059},
url = {https://doi.org/10.59628/jast.v2i4.1059},}
Enhancement Customer Loyalty Via Data Mining Techniques in Yemeni Banks: Review Study
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Sana'a University Journal
of Applied Sciences and Technology
A peer-reviewed, open-access journal publishing high-quality research in applied sciences, engineering, and technology since 2023.