Papers by Daniil Smirnov
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. |
Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling (2026.findings-acl)
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Ivan Rodkin, Daniil Orel, Konstantin Smirnov, Arman Bolatov, Bilal Elbouardi, Besher Hassan, Yuri Kuratov, Aydar Bulatov, Preslav Nakov, Timothy Baldwin, Artem Shelmanov, Mikhail Burtsev
| Challenge: | Reasoning is a core capability of large language models, yet how multi-step reasoning is learned and executed remains unclear. |
| Approach: | They evaluate how large language models learn multi-step reasoning without memorization . they find that most neural architectures trained from scratch can learn rule inference . |
| Outcome: | The proposed framework fails to solve a natural-language proxy task with high accuracy. |
Russian Learner Corpus: Towards Error-Cause Annotation for L2 Russian (2024.lrec-main)
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Daniil Kosakin, Sergei Obiedkov, Ivan Smirnov, Ekaterina Rakhilina, Anastasia Vyrenkova, Ekaterina Zalivina
| Challenge: | Russian Learner Corpus (RLC) is a large collection of learner texts written by native speakers of over forty languages. |
| Approach: | They propose an automatic error annotation tool that locates and labels errors according to a simplified version of the RLC error-type system. |
| Outcome: | The proposed tool locates and labels errors according to a simplified version of the RLC error-type system. |