Papers by Nikita Martynov
On the Way to Lossless Compression of Language Transformers: Exploring Cross-Domain Properties of Quantization (2024.lrec-main)
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Nikita Martynov, Aleksei Goncharov, Gleb Kumichev, Evgeniy Egorov, Stanislav Vladimirovich Pavlov, Mikhail Sergeevich Durinov, Aleksandr Sergeevich Zuev, Egor Anatolievich Filimonov
| Challenge: | Modern Natural Language Processing models have a huge capacity, but this makes it difficult to employ. |
| Approach: | They propose a method to quantize at least 95% of Transformer weights without access to task-specific data so the drop in performance does not exceed 0.02%. |
| Outcome: | The proposed method quantizes 95% of Transformer weights and corresponding activations to INT8 without access to task-specific data so the drop in performance does not exceed 0.02%. |
A Methodology for Generative Spelling Correction via Natural Spelling Errors Emulation across Multiple Domains and Languages (2024.findings-eacl)
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Nikita Martynov, Mark Baushenko, Anastasia Kozlova, Katerina Kolomeytseva, Aleksandr Abramov, Alena Fenogenova
| Challenge: | Recent advances in large language models have shown impressive text generation and language understanding capabilities, evident in benchmarks like SuperGLUE, GEM, BigBench etc. |
| Approach: | They propose a method for generative spelling correction that can be extended to any language with minor changes. |
| Outcome: | The proposed method can be extended to any language with minor changes, and is based on a set of generative models with a single-domain and multi-domain test sets. |
RuPAWS: A Russian Adversarial Dataset for Paraphrase Identification (2022.lrec-1)
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Nikita Martynov, Irina Krotova, Varvara Logacheva, Alexander Panchenko, Olga Kozlova, Nikita Semenov
| Challenge: | Existing datasets for paraphrase identification lack challenging sentence pairs with high word overlap. |
| Approach: | They propose to use a dataset for Russian paraphrase detection that includes examples from PAWS translated to the Russian language and manually annotated by native speakers. |
| Outcome: | The proposed model performs well on both datasets while maintaining accuracy on the ParaPhraser benchmark. |
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. |