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New Tetun medical QA dataset developed using LLM translation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Labadain-MedQA, a new dataset designed to improve medical question-answering capabilities in the Tetun language. The dataset was created by translating existing medical resources, such as MedAlpaca and WikiDoc, into Tetun using Gemini 2.5 Flash, resulting in over 39,000 question-answer pairs. Evaluations indicated that human translations were slightly superior to the LLM-generated ones in terms of medical accuracy and fluency. Fine-tuning Gemma and Llama models on this dataset demonstrated significant improvements in accuracy on Tetun-translated medical licensing exam questions, particularly when using parameter-efficient fine-tuning techniques like QLoRA. AI

IMPACT Facilitates cross-lingual medical knowledge transfer and improves LLM performance in low-resource languages.

RANK_REASON The cluster is about a research paper detailing the creation of a new dataset and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Tetun medical QA dataset developed using LLM translation

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The cluster is about a research paper detailing the creation of a new dataset and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Gabriel de Jesus, S\'ergio Nunes ·

    Labadain-MedQA: A Tetun Medical QA Dataset for Fine-Tuning and Evaluating Large Language Models

    arXiv:2611.00935v1 Announce Type: new 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-…

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