In-context learning is a method for improving machine translation in Large Language Models, but its performance is sensitive to the quality of the few-shot example selection. Current retrieval strategies use semantic similarity by computing sentence embeddings, and these methods often require significant computational overhead and specialised expertise. We evaluate the impact of retrieval strategies on translation performance in a specialised domain, comparing traditional, token-based fuzzy matching against semantic sentence embeddings. We use a medical corpus from the European Medicines Agency (EMEA) for the English-Romanian and English-German language pairs, and we evaluate translation quality with automatic metrics and manual evaluation. Our results show that 1-shot and 5-shot prompting significantly outperforms the 0-shot baselines for quality in automatic evaluations for both language pairs, and in manual evaluation for English-German. For the English-Romanian pair, the average scores of the manual evaluation for both quality and ranking follow the same trend, but with no statistical significance. Token-based fuzzy matching overwhelmingly has higher automatic quality scores than embedding-based retrieval.

Multilingual Neural Machine Translation (MNMT) models allow to translate across multiple languages based on only one system. We study the quality of a domain-adapted MNMT model in the medical domain for English-Romanian with automatic metrics and a human error typology annotation based on the Multidimensional Quality Metrics (MQM). We further expand the MQM typology to include terminology-specific error categories. We compare the out-of-domain MNMT with the in-domain adapted MNMT on a standard test dataset of abstracts from medical publications. The in-domain MNMT model outperforms the out-of-domain MNMT in all measured automatic metrics and produces fewer errors. In addition, we perform the manual annotation over the reference test dataset to study the quality of the reference translations. We identify a high number of omissions, additions, and mistranslations in the reference dataset, and comment on the assumed accuracy of existing datasets. Finally, we compare the correlation between the COMET, BERTScore, and chrF automatic metrics with the MQM annotated translations. COMET shows a better correlation with the MQM scores compared to the other metrics.