@inproceedings{roussis-etal-2025-krikri,
    title = "Krikri: Advancing Open Large Language Models for {G}reek",
    author = "Roussis, Dimitris  and
      Voukoutis, Leon  and
      Paraskevopoulos, Georgios  and
      Sofianopoulos, Sokratis  and
      Prokopidis, Prokopis  and
      Papavassileiou, Vassilis  and
      Katsamanis, Athanasios  and
      Piperidis, Stelios  and
      Katsouros, Vassilis",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.268/",
    doi = "10.18653/v1/2025.findings-emnlp.268",
    pages = "5012--5033",
    ISBN = "979-8-89176-335-7",
    abstract = "We introduce Llama-Krikri-8B, a cutting-edge Large Language Model tailored for the Greek language, built on Meta{'}s Llama 3.1-8B. Llama-Krikri-8B has been extensively trained on high-quality Greek data to ensure superior adaptation to linguistic nuances. With 8 billion parameters, it offers advanced capabilities while maintaining efficient computational performance. Llama-Krikri-8B supports both Modern Greek and English, and is also equipped to handle polytonic text and Ancient Greek. The chat version of Llama-Krikri-8B features a multi-stage post-training pipeline, utilizing both human and synthetic instruction and preference data, by applying techniques such as MAGPIE. In addition, for evaluation, we propose three novel public benchmarks for Greek. Our evaluation on existing as well as the proposed benchmarks shows notable improvements over comparable Greek and multilingual LLMs in both natural language understanding and generation as well as code generation."
}