Labadain-MedQA：用于微调和评估大语言模型的帝力语（Tetun）医疗问答数据集
Labadain-MedQA: A Tetun Medical QA Dataset for Fine-Tuning and Evaluating Large Language Models
AI 导读
研究者用 Gemini 2.5 Flash 将 MedAlpaca 的医学数据翻译成 Tetun 语，构建了 39,077 对医疗问答数据集 Labadain-MedQA。人工翻译质量评分高于 LLM 翻译（4.43/5 vs 4.02/5，p < 0.001）。用 QLoRA 微调 Gemma 4 12B 后，在 325 道 Tetun 版美国医师执照考试题上平均准确率相对提升 10.79%。
正文 · 原文
Abstract:Advances in large language models (LLMs) have created opportunities for cross-lingual transfer of medical knowledge from high-resource to low-resource languages to support access to healthcare information. However, medical question-answering (QA) datasets for fine-tuning and evaluating LLMs remain scarce for Tetun. To address this gap, we translated MedAlpaca's Medical Flashcards and WikiDoc Patient Information collections into Tetun using Gemini 2.5 Flash, producing 39,077 medical QA pairs. Translation quality was assessed on 50 randomly sampled QA pairs (100 documents) using automatic metrics against human reference translations and evaluations by two native Tetun-speaking healthcare professionals, focusing on medical accuracy and Tetun fluency. Human translations achieved higher mean ratings than LLM-generated translations for both criteria (4.43/5 vs. 4.02/5), with statistically significant differences (Wilcoxon signed-rank test, p < 0.001). To assess downstream utility, we fine-tuned Gemma and Llama variants on the translated MedAlpaca dataset using parameter-efficient fine-tuning (PEFT) and evaluated them on 325 Tetun-translated United States Medical Licensing Examination multiple-choice questions. Gemma 4 12B fine-tuned using QLoRA achieved relative improvements in mean accuracy of 10.79% with 4-bit NormalFloat (NF4) inference and 10.42% after merging the LoRA adapters into the BF16 base model, compared with the corresponding base models. These results highlight the viability of automatic dataset translation for Tetun medical QA and the effectiveness of PEFT in enhancing model performance. The datasets, models, and evaluation resources are accessible at this https URL to support reproducible research on Tetun medical QA and facilitate machine translation, cross lingual, and multilingual research.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.00935 [cs.CL] |
| (or arXiv:2611.00935v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.00935 arXiv-issued DOI via DataCite |
Submission history
From: Gabriel De Jesus [view email]
[v1]
Sat, 31 Oct 2026 13:36:05 UTC (222 KB)
[v2]
Wed, 4 Nov 2026 01:38:07 UTC (222 KB)
来源：arXiv cs.AI · arxiv.org