@inproceedings{chervyakov-etal-2026-multimodal,
title = "Multimodal Evaluation of {R}ussian-language Architectures",
author = "Chervyakov, Artem and
Isaeva, Ulyana and
Emelyanov, Anton and
Safin, Artem and
Tikhonova, Maria and
Kharitonov, Alexander and
Lyakh, Yulia and
Surovtsev, Petr and
Shevelev, Denis and
Saburov, Vildan and
Konovalov, Vasily and
Rykov, Elisei and
Sviridov, Ivan and
Miftakhova, Amina and
Alimova, Ilseyar and
Panchenko, Alexander and
Kapitanov, Alexander and
Fenogenova, Alena",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-long.94/",
pages = "2114--2161",
ISBN = "979-8-89176-380-7",
abstract = "Multimodal large language models (MLLMs) are currently at the center of research attention, showing rapid progress in scale and capabilities, yet their intelligence, limitations, and risks remain insufficiently understood. To address these issues, particularly in the context of the Russian language, where no multimodal benchmarks currently exist, we introduce MERA Multi, an open multimodal evaluation framework for Russian-spoken architectures. The benchmark is instruction-based and encompasses default text, image, audio, and video modalities, comprising 18 newly constructed evaluation tasks for both general-purpose models and modality-specific architectures (image-to-text, video-to-text, and audio-to-text). Our contributions include: (i) a universal taxonomy of multimodal abilities; (ii) 18 datasets created entirely from scratch with attention to Russian cultural and linguistic specificity, unified prompts, and metrics; (iii) baseline results for both closed-source and open-source models; (iv) a methodology for preventing benchmark leakage, including watermarking for private sets. While our current focus is on Russian, the proposed benchmark provides a replicable methodology for constructing multimodal benchmarks in typologically diverse languages, particularly within the Slavic language family."
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<abstract>Multimodal large language models (MLLMs) are currently at the center of research attention, showing rapid progress in scale and capabilities, yet their intelligence, limitations, and risks remain insufficiently understood. To address these issues, particularly in the context of the Russian language, where no multimodal benchmarks currently exist, we introduce MERA Multi, an open multimodal evaluation framework for Russian-spoken architectures. The benchmark is instruction-based and encompasses default text, image, audio, and video modalities, comprising 18 newly constructed evaluation tasks for both general-purpose models and modality-specific architectures (image-to-text, video-to-text, and audio-to-text). Our contributions include: (i) a universal taxonomy of multimodal abilities; (ii) 18 datasets created entirely from scratch with attention to Russian cultural and linguistic specificity, unified prompts, and metrics; (iii) baseline results for both closed-source and open-source models; (iv) a methodology for preventing benchmark leakage, including watermarking for private sets. While our current focus is on Russian, the proposed benchmark provides a replicable methodology for constructing multimodal benchmarks in typologically diverse languages, particularly within the Slavic language family.</abstract>
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%0 Conference Proceedings
%T Multimodal Evaluation of Russian-language Architectures
%A Chervyakov, Artem
%A Isaeva, Ulyana
%A Emelyanov, Anton
%A Safin, Artem
%A Tikhonova, Maria
%A Kharitonov, Alexander
%A Lyakh, Yulia
%A Surovtsev, Petr
%A Shevelev, Denis
%A Saburov, Vildan
%A Konovalov, Vasily
%A Rykov, Elisei
%A Sviridov, Ivan
%A Miftakhova, Amina
%A Alimova, Ilseyar
%A Panchenko, Alexander
%A Kapitanov, Alexander
%A Fenogenova, Alena
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F chervyakov-etal-2026-multimodal
%X Multimodal large language models (MLLMs) are currently at the center of research attention, showing rapid progress in scale and capabilities, yet their intelligence, limitations, and risks remain insufficiently understood. To address these issues, particularly in the context of the Russian language, where no multimodal benchmarks currently exist, we introduce MERA Multi, an open multimodal evaluation framework for Russian-spoken architectures. The benchmark is instruction-based and encompasses default text, image, audio, and video modalities, comprising 18 newly constructed evaluation tasks for both general-purpose models and modality-specific architectures (image-to-text, video-to-text, and audio-to-text). Our contributions include: (i) a universal taxonomy of multimodal abilities; (ii) 18 datasets created entirely from scratch with attention to Russian cultural and linguistic specificity, unified prompts, and metrics; (iii) baseline results for both closed-source and open-source models; (iv) a methodology for preventing benchmark leakage, including watermarking for private sets. While our current focus is on Russian, the proposed benchmark provides a replicable methodology for constructing multimodal benchmarks in typologically diverse languages, particularly within the Slavic language family.
%U https://aclanthology.org/2026.eacl-long.94/
%P 2114-2161
Markdown (Informal)
[Multimodal Evaluation of Russian-language Architectures](https://aclanthology.org/2026.eacl-long.94/) (Chervyakov et al., EACL 2026)
ACL
- Artem Chervyakov, Ulyana Isaeva, Anton Emelyanov, Artem Safin, Maria Tikhonova, Alexander Kharitonov, Yulia Lyakh, Petr Surovtsev, Denis Shevelev, Vildan Saburov, Vasily Konovalov, Elisei Rykov, Ivan Sviridov, Amina Miftakhova, Ilseyar Alimova, Alexander Panchenko, Alexander Kapitanov, and Alena Fenogenova. 2026. Multimodal Evaluation of Russian-language Architectures. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2114–2161, Rabat, Morocco. Association for Computational Linguistics.