@inproceedings{statkiewicz-etal-2026-annotation,
title = "Annotation-Efficient Vision-Language Model Adaptation to the {P}olish Language Using the {LL}a{VA} Framework",
author = "Statkiewicz, Grzegorz and
Dobrzeniecka, Alicja and
Seweryn, Karolina and
Krasnod{\k{e}}bska, Aleksandra and
Piosek, Karolina and
Bogusz, Katarzyna and
Cygert, Sebastian and
Kusa, Wojciech",
editor = "Baez Santamaria, Selene and
Somayajula, Sai Ashish and
Yamaguchi, Atsuki",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 4: Student Research Workshop)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-srw.44/",
pages = "569--589",
ISBN = "979-8-89176-383-8",
abstract = "Most vision-language models (VLMs) are trained on English-centric data, limiting their performance in other languages and cultural contexts. This restricts their usability for non-English-speaking users and hinders the development of multimodal systems that reflect diverse linguistic and cultural realities. In this work, we reproduce and adapt the LLaVA-Next methodology to create a set of Polish VLMs. We rely on a fully automated pipeline for translating and filtering existing multimodal datasets, and complement this with synthetic Polish data for OCR and culturally specific tasks. Despite relying almost entirely on automatic translation and minimal manual intervention, our approach yields strong results: we observe a +9.5 pp improvement over LLaVA-1.6-Vicuna-13B on a Polish-adapted MMBench, along with higher-quality captions in generative evaluations, as measured by human annotators in terms of linguistic correctness. These findings highlight that large-scale automated translation, combined with lightweight filtering, can effectively bootstrap high-quality multimodal models for low-resource languages. Some challenges remain, particularly in cultural coverage and evaluation. To facilitate further research, we release our models, code, and datasets."
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<abstract>Most vision-language models (VLMs) are trained on English-centric data, limiting their performance in other languages and cultural contexts. This restricts their usability for non-English-speaking users and hinders the development of multimodal systems that reflect diverse linguistic and cultural realities. In this work, we reproduce and adapt the LLaVA-Next methodology to create a set of Polish VLMs. We rely on a fully automated pipeline for translating and filtering existing multimodal datasets, and complement this with synthetic Polish data for OCR and culturally specific tasks. Despite relying almost entirely on automatic translation and minimal manual intervention, our approach yields strong results: we observe a +9.5 pp improvement over LLaVA-1.6-Vicuna-13B on a Polish-adapted MMBench, along with higher-quality captions in generative evaluations, as measured by human annotators in terms of linguistic correctness. These findings highlight that large-scale automated translation, combined with lightweight filtering, can effectively bootstrap high-quality multimodal models for low-resource languages. Some challenges remain, particularly in cultural coverage and evaluation. To facilitate further research, we release our models, code, and datasets.</abstract>
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%0 Conference Proceedings
%T Annotation-Efficient Vision-Language Model Adaptation to the Polish Language Using the LLaVA Framework
%A Statkiewicz, Grzegorz
%A Dobrzeniecka, Alicja
%A Seweryn, Karolina
%A Krasnodębska, Aleksandra
%A Piosek, Karolina
%A Bogusz, Katarzyna
%A Cygert, Sebastian
%A Kusa, Wojciech
%Y Baez Santamaria, Selene
%Y Somayajula, Sai Ashish
%Y Yamaguchi, Atsuki
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-383-8
%F statkiewicz-etal-2026-annotation
%X Most vision-language models (VLMs) are trained on English-centric data, limiting their performance in other languages and cultural contexts. This restricts their usability for non-English-speaking users and hinders the development of multimodal systems that reflect diverse linguistic and cultural realities. In this work, we reproduce and adapt the LLaVA-Next methodology to create a set of Polish VLMs. We rely on a fully automated pipeline for translating and filtering existing multimodal datasets, and complement this with synthetic Polish data for OCR and culturally specific tasks. Despite relying almost entirely on automatic translation and minimal manual intervention, our approach yields strong results: we observe a +9.5 pp improvement over LLaVA-1.6-Vicuna-13B on a Polish-adapted MMBench, along with higher-quality captions in generative evaluations, as measured by human annotators in terms of linguistic correctness. These findings highlight that large-scale automated translation, combined with lightweight filtering, can effectively bootstrap high-quality multimodal models for low-resource languages. Some challenges remain, particularly in cultural coverage and evaluation. To facilitate further research, we release our models, code, and datasets.
%U https://aclanthology.org/2026.eacl-srw.44/
%P 569-589
Markdown (Informal)
[Annotation-Efficient Vision-Language Model Adaptation to the Polish Language Using the LLaVA Framework](https://aclanthology.org/2026.eacl-srw.44/) (Statkiewicz et al., EACL 2026)
ACL
- Grzegorz Statkiewicz, Alicja Dobrzeniecka, Karolina Seweryn, Aleksandra Krasnodębska, Karolina Piosek, Katarzyna Bogusz, Sebastian Cygert, and Wojciech Kusa. 2026. Annotation-Efficient Vision-Language Model Adaptation to the Polish Language Using the LLaVA Framework. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 569–589, Rabat, Morocco. Association for Computational Linguistics.