从字节到罗宋汤：针对乌克兰语表示微调Gemma与Mistral
From Bytes to Borsch: Fine-Tuning Gemma and Mistral for the Ukrainian Language Representation
AI总结：
针对乌克兰语等低资源语言在大语言模型中表示不足的问题，本文微调开源Gemma和Mistral模型，发布UKID数据集，提升模型乌克兰语能力，推动AI语言多样性与数字包容性。
摘要：
在人工智能与自然语言处理（NLP）快速发展的领域中，生成式大语言模型（LLMs）处于创新前沿，展现出无与伦比的文本理解与生成能力。然而，乌克兰语等低资源语言的表示十分有限，构成了显著挑战，限制了这项技术的覆盖范围与适用性。本文通过使用乌克兰语数据集微调开源的Gemma和Mistral大语言模型来解决这一问题，旨在提升二者的语言能力，并将其与其他现有可处理乌克兰语的模型进行基准测试。这项工作不仅旨在缓解技术中的语言偏见，还能促进数字领域的包容性。我们采用透明且可复现的方法，为后续NLP研究与开发提供支持。此外，我们还发布了乌克兰语知识与指令数据集（Ukrainian Knowledge and Instruction Dataset, UKID），为未来的语言模型微调工作提供助力。本研究不仅推动了NLP领域的发展，还凸显了人工智能中语言多样性的重要性——这对于文化保护、教育以及拓展AI的全球实用性至关重要。最终，我们倡导构建一个技术包容的未来，让AI能够跨越所有语言有效沟通，尤其是那些当前代表性不足的语言。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
