Machine translation has tremendous potential in connecting people and cultures. The Ukrainian language has an extensive collection of myths, legends, proverbs, songs, and folktales. They all represent the emotions, beliefs, and world views of Ukrainians. In this paper, we focus on several widely known Ukrainian folktales, all of which are anonymous due to the nature of this genre. Furthermore, folktales are usually passed on from one generation to another, going back hundreds of years. Interestingly, many available translations are rather transcreations, in which stories are retold and adapted to the target language and culture. We believe machine translation can be a useful supplemental tool in translating Ukrainian folklore, creating opportunities for more research and knowledge transfer about the Ukrainian language and culture. The first step in improving the machine translation performance of Ukrainian folktales is the creation of a highquality corpus that addresses domain-specific nuances and challenges.

An image is often said to be worth a thousand words, and certain images can tell rich and insightful stories. Can these stories be told via image captioning? Images from folklore genres, such as mythology, folk dance, cultural signs, and symbols, are vital to every culture. Our research compares the performance of four popular vision-language models (GPT-4V, Gemini Pro Vision, LLaVA, and OpenFlamingo) in identifying culturally specific information in such images and creating accurate and culturally sensitive image captions. We also propose a new evaluation metric, Cultural Awareness Score (CAS), dedicated to measuring the degree of cultural awareness in image captions. We provide a dataset MOSAIC-1.5k, labeled with ground truth for images containing cultural background and context, as well as a labeled dataset with assigned Cultural Awareness Scores that can be used with unseen data. Creating culturally appropriate image captions is valuable for scientific research and can be beneficial for many practical applications. We envision that our work will promote a deeper integration of cultural sensitivity in AI applications worldwide. By making the dataset and Cultural Awareness Score available to the public, we aim to facilitate further research in this area, encouraging the development of more culturally aware AI systems that respect and celebrate global diversity.