In the rapidly advancing field of AI and NLP, generative large language
models (LLMs) stand at the forefront of innovation, showcasing unparalleled
abilities in text understanding and generation. However, the limited
representation of low-resource languages like Ukrainian poses a notable
challenge, restricting the reach and relevance of this technology. Our paper
addresses this by fine-tuning the open-source Gemma and Mistral LLMs with
Ukrainian datasets, aiming to improve their linguistic proficiency and
benchmarking them against other existing models capable of processing Ukrainian
language. This endeavor not only aims to mitigate language bias in technology
but also promotes inclusivity in the digital realm. Our transparent and
reproducible approach encourages further NLP research and development.
Additionally, we present the Ukrainian Knowledge and Instruction Dataset (UKID)
to aid future efforts in language model fine-tuning. Our research not only
advances the field of NLP but also highlights the importance of linguistic
diversity in AI, which is crucial for cultural preservation, education, and
expanding AI's global utility. Ultimately, we advocate for a future where
technology is inclusive, enabling AI to communicate effectively across all
languages, especially those currently underrepresented.

利用乌克兰数据集对开源 Gemme 和 Mistral 语言模型进行微调，以提高其语言能力，并与其他处理乌克兰语的模型进行基准比较。该研究旨在减少技术中的语言偏见，促进数字领域的包容性。通过透明且可复现的方法鼓励进一步的 NLP 研究与发展，同时提出乌克兰知识与指令数据集（UKID）以支持未来的语言模型微调工作。本研究推动了自然语言处理领域的发展，并凸显了 AI 中语言多样性的重要性，对文化保护、教育和全球 AI 的扩展至关重要。最终，我们呼吁实现一种包容性的技术未来，使 AI 能够在所有语言，特别是目前少数代表的语言中有效地进行交流。