Can Large Language Models translate texts with rich cultural elements? How “cultured” are they? This paper provides an overview of an experiment in Machine Translation of Ukrainian folktales using Large Language Models (Open AI), Google Cloud Translation API, and Opus MT. After benchmarking their performance, we have fine-tuned an Opus MT model on a domain-specific small dataset specially created to translate folktales from Ukrainian to English. We have also tested various prompt engineering techniques on the new Open AI models to generate translations of our test dataset (folktale ‘The Mitten’) and have observed promising results. This research explores the importance of both small data and Large Language Models in Machine Learning, specifically in Machine Translation of literary texts, on the example of Ukrainian folktales.

Folktales are linguistically very rich and culturally significant in understanding the source language. Historically, only human translation has been used for translating folklore. Therefore, the number of translated texts is very sparse, which limits access to knowledge about cultural traditions and customs. We have created a new Ukrainian-To-English parallel corpus of familiar Ukrainian folktales based on available English translations and suggested several new ones. We offer a combined domain-specific approach to building and augmenting this corpus, considering the nature of the domain and differences in the purpose of human versus machine translation. Our corpus is word and sentence-aligned, allowing for the best curation of meaning, specifically tailored for use as training data for machine translation models.