Papers by Nikolai Gerasimenko
RusCode: Russian Cultural Code Benchmark for Text-to-Image Generation (2025.findings-naacl)
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Viacheslav Vasilev, Julia Agafonova, Nikolai Gerasimenko, Alexander Kapitanov, Polina Mikhailova, Evelina Mironova, Denis Dimitrov
| Challenge: | Text-to-image generation models exhibit a strong bias toward English-speaking cultures, ignoring or misrepresenting the unique characteristics of other language groups, countries, and nationalities. |
| Approach: | They propose a RusCode benchmark to evaluate the quality of text-to-image generation containing elements of the Russian cultural code. |
| Outcome: | The proposed model is based on 1250 text prompts in Russian and their translations into English. |
Your Transformer is Secretly Linear (2024.acl-long)
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Anton Razzhigaev, Matvey Mikhalchuk, Elizaveta Goncharova, Nikolai Gerasimenko, Ivan Oseledets, Denis Dimitrov, Andrey Kuznetsov
| Challenge: | a novel linear characteristic exclusive to transformer decoders is revealed: embedding transformations between sequential layers exhibit almost perfect linearity. |
| Approach: | They propose a cosine-similarity-based regularization to reduce layer linearity in transformer decoders. |
| Outcome: | The proposed method improves performance metrics on Tiny Stories and SuperGLUE but also decreases the linearity of the models. |
Kandinsky 3: Text-to-Image Synthesis for Multifunctional Generative Framework (2024.emnlp-demo)
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Arkhipkin Vladimir, Viacheslav Vasilev, Andrei Filatov, Igor Pavlov, Julia Agafonova, Nikolai Gerasimenko, Anna Averchenkova, Evelina Mironova, Bukashkin Anton, Konstantin Kulikov, Andrey Kuznetsov, Denis Dimitrov
| Challenge: | Text-to-image (T2I) diffusion models are popular for image manipulation, but also for video generation. |
| Approach: | They propose a novel T2I diffusion model based on latent diffusion that extends the base model for various applications. |
| Outcome: | The proposed model achieves high quality and photorealism and is 3 times faster than the base model. |