Papers by Artem Chervyakov
GigaChat Family: Efficient Russian Language Modeling Through Mixture of Experts Architecture (2025.acl-demo)
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Valentin Mamedov, Evgenii Kosarev, Gregory Leleytner, Ilya Shchuckin, Valeriy Berezovskiy, Daniil Smirnov, Dmitry Kozlov, Sergei Averkiev, Lukyanenko Ivan, Aleksandr Proshunin, Ainur Israfilova, Ivan Baskov, Artem Chervyakov, Emil Shakirov, Mikhail Kolesov, Daria Khomich, Daria Latortseva, Sergei Porkhun, Yury Fedorov, Oleg Kutuzov, Polina Kudriavtseva, Sofiia Soldatova, Kolodin Egor, Stanislav Pyatkin, Dzmitry Menshykh, Grafov Sergei IUrevich, Eldar Damirov, Vladimir Karlov, Ruslan Gaitukiev, Arkadiy Shatenov, Alena Fenogenova, Nikita Savushkin, Fedor Minkin
| Challenge: | generative large language models have become crucial for modern NLP research and applications across multiple languages. |
| Approach: | They introduce the GigaChat family of Russian LLMs, available in various sizes . they evaluate their performance on Russian and English benchmarks and compare them with multilingual analogs . |
| Outcome: | The proposed model family is available in various sizes and is tested on Russian and English benchmarks. |
MERA: A Comprehensive LLM Evaluation in Russian (2024.acl-long)
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Alena Fenogenova, Artem Chervyakov, Nikita Martynov, Anastasia Kozlova, Maria Tikhonova, Albina Akhmetgareeva, Anton Emelyanov, Denis Shevelev, Pavel Lebedev, Leonid Sinev, Ulyana Isaeva, Katerina Kolomeytseva, Daniil Moskovskiy, Elizaveta Goncharova, Nikita Savushkin, Polina Mikhailova, Anastasia Minaeva, Denis Dimitrov, Alexander Panchenko, Sergey Markov
| Challenge: | Recent advances in foundation models have led to the emergence of powerful Large Language Models (LLMs), which showcase unprecedented tasksolving capabilities. |
| Approach: | They propose a method to evaluate FMs and LMs in fixed zero- and few-shot instruction settings that can be extended to other modalities. |
| Outcome: | The proposed evaluation methodology includes an open-source code base and a leaderboard with a submission system. |
Multimodal Evaluation of Russian-language Architectures (2026.eacl-long)
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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, Alena Fenogenova
| Challenge: | Multimodal large language models (MLLMs) are at the center of research attention, yet intelligence, limitations, and risks remain insufficiently understood. |
| Approach: | They propose an open multimodal evaluation framework for Russian-spoken architectures . the framework is instruction-based and includes 18 newly constructed evaluation tasks . |
| Outcome: | The proposed framework provides a replicable methodology for constructing multimodal benchmarks in Russian-spoken architectures. |