A Comparative Evaluation of Open-Source Models for Russian-Kazakh Translation

Gleb Shanshin


Abstract
We describe an evaluation of several open-source models under identical inference conditions without task-specific training. Despite covering a wide range of available models, including both multilingual systems and models specifically designed for Russian-Kazakh translation, the results indicate that the highest performance is achieved by the language-specific approach.
Anthology ID:
2026.loresmt-1.19
Volume:
Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2026)
Month:
March
Year:
2026
Address:
Rabat, Morocco
Editors:
Atul Kr. Ojha, Chao-hong Liu, Ekaterina Vylomova, Flammie Pirinen, Jonathan Washington, Nathaniel Oco, Xiaobing Zhao
Venues:
LoResMT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
213–216
Language:
URL:
https://aclanthology.org/2026.loresmt-1.19/
DOI:
Bibkey:
Cite (ACL):
Gleb Shanshin. 2026. A Comparative Evaluation of Open-Source Models for Russian-Kazakh Translation. In Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2026), pages 213–216, Rabat, Morocco. Association for Computational Linguistics.
Cite (Informal):
A Comparative Evaluation of Open-Source Models for Russian-Kazakh Translation (Shanshin, LoResMT 2026)
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PDF:
https://aclanthology.org/2026.loresmt-1.19.pdf