@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Krikri: Advancing Open Large Language Models for Greek
%A Roussis, Dimitris
%A Voukoutis, Leon
%A Paraskevopoulos, Georgios
%A Sofianopoulos, Sokratis
%A Prokopidis, Prokopis
%A Papavassileiou, Vassilis
%A Katsamanis, Athanasios
%A Piperidis, Stelios
%A Katsouros, Vassilis
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F roussis-etal-2025-krikri
%X 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.
%R 10.18653/v1/2025.findings-emnlp.268
%U https://aclanthology.org/2025.findings-emnlp.268/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.268
%P 5012-5033
Markdown (Informal)
[Krikri: Advancing Open Large Language Models for Greek](https://aclanthology.org/2025.findings-emnlp.268/) (Roussis et al., Findings 2025)
ACL
- Dimitris Roussis, Leon Voukoutis, Georgios Paraskevopoulos, Sokratis Sofianopoulos, Prokopis Prokopidis, Vassilis Papavassileiou, Athanasios Katsamanis, Stelios Piperidis, and Vassilis Katsouros. 2025. Krikri: Advancing Open Large Language Models for Greek. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 5012–5033, Suzhou, China. Association for Computational Linguistics.