Large Language Models (LLMs) have demonstrated strong capabilities in multilingual machine translation. However, they underperform for low-resource languages, indicating the need for more explicit instructional guidance. In this work, we introduce Fragment-Shot Prompting, a novel few-shot prompting method that aims to retrieve examples for every word occurring in the sentence to be translated, illustrating their use and meaning in context. We evaluate our method on translation between Italian, Ladin (Val Badia) and Ladin (Gherdëina) and compare its performance with zero-shot prompting, random few-shot prompting, as well as established lexical and semantic retrieval strategies. We conduct these experiments using state-of-the-art LLMs, including GPT-3.5, GPT-4o, o1-mini, LlaMA-3.3, and DeepSeek-R1. Our results demonstrate that LLMs can extract substantial value from limited data when translating from a low- to the high-resource language. However, this does not apply to translations into the low-resource languages, where the prompting method plays a much more important role. In particular, our method consistently delivers the best results and enables significant gains. Even though translation performance into Ladin remains limited with the available resources, our results highlight the importance of syntactic coverage for improving translation accuracy and ariant-specific adaptation in low-resource scenarios.

Recent advances in neural machine translation (NMT) have opened new possibilities for developing translation systems also for smaller, so-called low-resource, languages. The rise of large language models (LLMs) has further revolutionized machine translation by enabling more flexible and context-aware generation. However, many challenges remain for low-resource languages, and the availability of high-quality, validated test data is essential to support meaningful development, evaluation, and comparison of translation systems. In this work, we present an extension of the FLORES+ dataset for two Ladin variants, Val Badia and Gherdëina, as a submission to the Open Language Data Initiative Shared Task 2025. To complement existing resources, we additionally release two parallel datasets for Gherdëina–Val Badia and Gherdëina–Italian. We validate these datasets by evaluating state-of-the-art LLMs and NMT systems on this test data, both with and without leveraging the newly released parallel data for fine-tuning and prompting. The results highlight the considerable potential for improving translation quality in Ladin, while also underscoring the need for further research and resource development, for which this contribution provides a basis.