In this paper we introduce the first effort to adapt large language models (LLMs) to the Ukrainian dialect (in our case Hutsul), a low-resource and morphologically complex dialect spoken in the Carpathian Highlands. We created a parallel corpus of 9852 dialect-to-standard Ukrainian sentence pairs and a dictionary of 7320 dialectal word mappings. We also addressed data shortage by proposing an advanced Retrieval-Augmented Generation (RAG) pipeline to generate synthetic parallel translation pairs, expanding the corpus with 52142 examples. We have fine-tuned multiple open-source LLMs using LoRA and evaluated them on a standard-to-dialect translation task, also comparing with few-shot GPT-4o translation. In the absence of human annotators, we adopt a multi-metric evaluation strategy combining BLEU, chrF++, TER, and LLM-based judgment (GPT-4o). The results show that even small(7B) finetuned models outperform zero-shot baselines such as GPT-4o across both automatic and LLM-evaluated metrics. All data, models, and code are publicly released at: https://github.com/woters/vuyko-hutsul.

This paper presents the results of the UNLP 2025 Shared Task on Detecting Social Media Manipulation. The task included two tracks: Technique Classification and Span Identification. The benchmark dataset contains 9,557 posts from Ukrainian Telegram channels manually annotated by media experts. A total of 51 teams registered, 22 teams submitted systems, and 595 runs were evaluated on a hidden test set via Kaggle. Performance was measured with macro F1 for classification and token‐level F1 for identification. The shared task provides the first publicly available benchmark for manipulation detection in Ukrainian social media and highlights promising directions for low‐resource propaganda research. The Kaggle leaderboard is left open for further submissions.

This paper presents the results of the UNLP 2024 shared task, the first Shared Task on Fine-Tuning Large Language Models for the Ukrainian language. The goal of the task was to facilitate the creation of models that have knowledge of the Ukrainian language, history, and culture, as well as common knowledge, and are capable of generating fluent and accurate responses in Ukrainian. The participants were required to use models with open weights and reasonable size to ensure the reproducibility of the solutions. The participating systems were evaluated using multiple-choice exam questions and manually crafted open questions. Three teams submitted their solutions before the deadline, and two teams submitted papers that were accepted to appear in the UNLP workshop proceedings and are referred to in this report. The Codabench leaderboard is left open for further submissions.