Multilingual LLM Prompting Strategies for Medical English-Vietnamese Machine Translation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908606596644864 |
|---|---|
| author | Vo, Nhu Le, Nu-Uyen-Phuong Le, Dung D. Piccardi, Massimo Buntine, Wray |
| author_facet | Vo, Nhu Le, Nu-Uyen-Phuong Le, Dung D. Piccardi, Massimo Buntine, Wray |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15640 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multilingual LLM Prompting Strategies for Medical English-Vietnamese Machine Translation Vo, Nhu Le, Nu-Uyen-Phuong Le, Dung D. Piccardi, Massimo Buntine, Wray Computation and Language 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. |
| title | Multilingual LLM Prompting Strategies for Medical English-Vietnamese Machine Translation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.15640 |