Text-to-SQL for Low-Resource Languages: A Unified Large Language Model Framework Evaluated on Arabic, Japanese, and Vietnamese

Text-to-SQL research has concentrated on English, while prior work on Arabic, Japanese, and Vietnamese has largely relied on grammar-constrained parsers or zero-shot prompting, with recent work beginning to explore trained LLM-based alternatives. We investigate whether a unified fine-tuned open-source pipeline can serve typologically distant low-resource languages and quantify the same-pipeline English-to-target gap on parallel Spider instances. We fine-tune Qwen2.5-Coder-7B-Instruct with QLoRA using shared schema grounding, value grounding, an English translation hint, and execution-based self-consistency voting over eight candidates. Architecture, constants, and inference are fixed across languages, while each language uses its own training data and adapter. On Ar-Spider, MultiSpider-JA, and MultiSpider-VI, the pipeline reaches 75–80% execution accuracy (EX), with raw exact set match (ESM) within 0.7 points of the strongest prior Arabic parser and above the strongest multilingual MultiSpider baseline for Japanese (+2.5) and Vietnamese (+3.5). An English same-pipeline run (85.3% EX) yields gaps of −10.1, −10.5, and −5.4 percentage points for Arabic, Japanese, and Vietnamese, respectively. Removing the complete value-grounding group causes the largest degradation. The EX–ESM gap persists without voting and repair, indicating differences in generated SQL rather than decoder effects; we therefore recommend reporting both metrics.