Multilingual LLM Prompting Strategies for Medical English-Vietnamese Machine Translation

• URL: http://arxiv.org/abs/2509.15640v2
• Date: Thu, 23 Oct 2025 06:55:37 GMT
• Title: Multilingual LLM Prompting Strategies for Medical English-Vietnamese Machine Translation
• Authors: Nhu Vo, Nu-Uyen-Phuong Le, Dung D. Le, Massimo Piccardi, Wray Buntine,
• Abstract summary: Medical English-Vietnamese machine translation (En-Vi MT) is essential for healthcare access and communication in Vietnam.<n>We evaluate prompting strategies for six multilingual LLMs (0.5B-9B parameters) on the MedEV dataset.
• Score: 7.238888652441979
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical English-Vietnamese machine translation (En-Vi MT) is essential for healthcare access and communication in Vietnam, yet Vietnamese remains a low-resource and under-studied language. We systematically evaluate prompting strategies for six multilingual LLMs (0.5B-9B parameters) on the MedEV dataset, comparing zero-shot, few-shot, and dictionary-augmented prompting with Meddict, an English-Vietnamese medical lexicon. Results show that model scale is the primary driver of performance: larger LLMs achieve strong zero-shot results, while few-shot prompting yields only marginal improvements. In contrast, terminology-aware cues and embedding-based example retrieval consistently improve domain-specific translation. These findings underscore both the promise and the current limitations of multilingual LLMs for medical En-Vi MT.

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