Enhancing Text-to-SQL Conversion in Turkish: An Analysis of LLMs with Schema Context

Abstract

The task of converting natural language text to SQL queries has gained significant attention, particularly with the advancement of large language models (LLMs). However, research focused on text-to-SQL systems in non-English languages, such as Turkish, remains limited. This paper presents a comparative study evaluating the performance of LLMs, including GPT-3.5 Turbo, T5, and SQLCoder, on the TUR2SQL dataset-a cross-domain Turkish text-to-SQL dataset. Our experiments highlight the critical role of schema context in enhancing the accuracy of SQL generation, particularly for the T5 model, which showed significant improvements in logical-form and execution accuracy when fine-tuned with schema context. This study provides valuable insights into the challenges and potential of applying LLMs to Turkish text-to-SQL tasks, underscoring the importance of model fine-tuning and schema linking for accurate S Q L query generation in low-resource languages. © 2025 Elsevier B.V., All rights reserved.