publications | Viktor Moskvoretskii

2025

Will It Still Be True Tomorrow? Multilingual Evergreen Question Classification to Improve Trustworthy QA

Sergey Pletenev, Maria Marina, 6 more authors, Viktor Moskvoretskii

In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (Main Conference)

arXiv Bib

@inproceedings{pletenev2025will,
  title     = {Will It Still Be True Tomorrow? Multilingual Evergreen Question Classification to Improve Trustworthy QA},
  author    = {Pletenev, Sergey and Marina, Maria and Ivanov, Nikolay and Galimzianova, Daria and Krayko, Nikita and Salnikov, Mikhail and Konovalov, Vasily and Panchenko, Alexander and Moskvoretskii, Viktor},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (Main Conference)},
  year      = {2025},
  url       = {https://arxiv.org/abs/2505.21115}
}

LLM-Independent Adaptive RAG: Let the Question Speak for Itself

Maria Marina, Nikolay Ivanov, 6 more authors, Viktor Moskvoretskii

In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (Main Conference)

arXiv Bib

@misc{marina2025llmindependentadaptiveraglet,
  title        = {LLM-Independent Adaptive RAG: Let the Question Speak for Itself},
  author       = {Marina, Maria and Ivanov, Nikolay and Pletenev, Sergey and Salnikov, Mikhail and Galimzianova, Daria and Krayko, Nikita and Konovalov, Vasily and Panchenko, Alexander and Moskvoretskii, Viktor},
  year         = {2025},
  eprint       = {2505.04253},
  archivePrefix= {arXiv},
  primaryClass = {cs.CL},
  url          = {https://arxiv.org/abs/2505.04253}
}

Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home

Viktor Moskvoretskii, Maria Marina, 8 more authors

In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jul 2025

ACL Anthology Bib

@inproceedings{moskvoretskii-etal-2025-adaptive,
  title     = {Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home},
  author    = {Moskvoretskii, Viktor and Marina, Maria and Salnikov, Mikhail and Ivanov, Nikolay and Pletenev, Sergey and Galimzianova, Daria and Krayko, Nikita and Konovalov, Vasily and Nikishina, Irina and Panchenko, Alexander},
  editor    = {Che, Wanxiang and Nabende, Joyce and Shutova, Ekaterina and Pilehvar, Mohammad Taher},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = jul,
  year      = {2025},
  address   = {Vienna, Austria},
  publisher = {Association for Computational Linguistics},
  url       = {https://aclanthology.org/2025.acl-long.319/},
  doi       = {10.18653/v1/2025.acl-long.319},
  pages     = {6355--6384},
  ISBN      = {979-8-89176-251-0}
}

GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs

Maxim Zhelnin, Viktor Moskvoretskii, Egor Shvetsov, 4 more authors

In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jul 2025

ACL Anthology Bib

@inproceedings{zhelnin-etal-2025-gift,
  title     = {{GIFT}-{SW}: {G}aussian noise Injected Fine-Tuning of Salient Weights for {LLM}s},
  author    = {Zhelnin, Maxim and Moskvoretskii, Viktor and Shvetsov, Egor and Krylova, Maria and Egor, Venediktov and Aleksandr, Zuev and Burnaev, Evgeny},
  editor    = {Che, Wanxiang and Nabende, Joyce and Shutova, Ekaterina and Pilehvar, Mohammad Taher},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = jul,
  year      = {2025},
  address   = {Vienna, Austria},
  publisher = {Association for Computational Linguistics},
  url       = {https://aclanthology.org/2025.acl-long.324/},
  doi       = {10.18653/v1/2025.acl-long.324},
  pages     = {6463--6480},
  ISBN      = {979-8-89176-251-0}
}

Self-Taught Self-Correction for Small Language Models

Viktor Moskvoretskii, Chris Biemann, Irina Nikishina

ICLR 2025 Workshop Scaling Self-Improving Foundation Models without Human Supervision (SSI-FM), Apr 2025

arXiv Bib

@article{moskvoretskii2025self,
  title   = {Self-Taught Self-Correction for Small Language Models},
  author  = {Moskvoretskii, Viktor and Biemann, Chris and Nikishina, Irina},
  journal = {arXiv preprint arXiv:2503.08681},
  year    = {2025},
  url     = {https://arxiv.org/abs/2503.08681}
}

Challenges of Multi-Modal Coreset Selection for Depth Prediction

Viktor Moskvoretskii, Narek Alvandian

ICLR 2025 Workshop on I Can’t Believe It’s Not Better (ICBINB), Apr 2025

arXiv Bib

@inproceedings{moskvoretskii2025challenges,
  title     = {Challenges of Multi-Modal Coreset Selection for Depth Prediction},
  author    = {Moskvoretskii, Viktor and Alvandian, Narek},
  booktitle = {ICLR 2025 Workshop on I Can't Believe It's Not Better (ICBINB)},
  year      = {2025},
  month     = apr,
  url       = {https://arxiv.org/abs/2502.15834}
}

EBES: Easy Benchmarking for Event Sequences

Dmitry Osin, Igor Udovichenko, Egor Shvetsov, Viktor Moskvoretskii, Evgeny Burnaev

In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2, 2025

Bib

@inproceedings{osin2025ebes,
  title     = {EBES: Easy Benchmarking for Event Sequences},
  author    = {Osin, Dmitry and Udovichenko, Igor and Shvetsov, Egor and Moskvoretskii, Viktor and Burnaev, Evgeny},
  booktitle = {Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2},
  pages     = {5730--5741},
  year      = {2025}
}

Do I look like a cat. n. 01 to you? A Taxonomy Image Generation Benchmark

Viktor Moskvoretskii, Alina Lobanova, 4 more authors

arXiv preprint, 2025

arXiv Bib

@article{moskvoretskii2025look,
  title   = {Do I look like a cat. n. 01 to you? A Taxonomy Image Generation Benchmark},
  author  = {Moskvoretskii, Viktor and Lobanova, Alina and Neminova, Ekaterina and Biemann, Chris and Panchenko, Alexander and Nikishina, Irina},
  journal = {arXiv preprint arXiv:2503.10357},
  year    = {2025},
  url     = {https://arxiv.org/abs/2503.10357}
}

Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models

Artyom Kharinaev, Viktor Moskvoretskii, 4 more authors

arXiv preprint, 2025

arXiv Bib

@article{kharinaev2025investigating,
  title   = {Investigating the impact of quantization methods on the safety and reliability of large language models},
  author  = {Kharinaev, Artyom and Moskvoretskii, Viktor and Shvetsov, Egor and Studenikina, Kseniia and Mikhail, Bykov and Burnaev, Evgeny},
  journal = {arXiv preprint arXiv:2502.15799},
  year    = {2025},
  url     = {https://arxiv.org/abs/2502.15799}
}

Argument-Based Comparative Question Answering Evaluation Benchmark

Irina Nikishina, 4 more authors, Viktor Moskvoretskii, 3 more authors

arXiv preprint, 2025

arXiv Bib

@article{nikishina2025argument,
  title   = {Argument-Based Comparative Question Answering Evaluation Benchmark},
  author  = {Nikishina, Irina and Anwar, Saba and Dolgov, Nikolay and Manina, Maria and Ignatenko, Daria and Moskvoretskii, Viktor and Shelmanov, Artem and Baldwin, Tim and Biemann, Chris},
  journal = {arXiv preprint arXiv:2502.14476},
  year    = {2025},
  url     = {https://arxiv.org/abs/2502.14476}
}

2024

Low-Resource Machine Translation through the Lens of Personalized Federated Learning

Viktor Moskvoretskii, Nazarii Tupitsa, Chris Biemann, 3 more authors

In Findings of the Association for Computational Linguistics: EMNLP 2024, Nov 2024

ACL Anthology Code Bib

@inproceedings{moskvoretskii-etal-2024-low,
  title     = {Low-Resource Machine Translation through the Lens of Personalized Federated Learning},
  author    = {Moskvoretskii, Viktor and Tupitsa, Nazarii and Biemann, Chris and Horv{\'a}th, Samuel and Gorbunov, Eduard and Nikishina, Irina},
  editor    = {Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2024},
  month     = nov,
  year      = {2024},
  address   = {Miami, Florida, USA},
  publisher = {Association for Computational Linguistics},
  url       = {https://aclanthology.org/2024.findings-emnlp.514/},
  doi       = {10.18653/v1/2024.findings-emnlp.514},
  pages     = {8806--8825}
}

TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks

Viktor Moskvoretskii, Ekaterina Neminova, Alina Lobanova, and 2 more authors

In Proceedings of the 62nd Conference of the Association for Computational Linguistics, Aug 2024

Bib

@inproceedings{moskvoretskii-etal-2024-taxollama,
  title = {TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks},
  author = {Moskvoretskii, Viktor and Neminova, Ekaterina and Lobanova, Alina and Panchenko, Alexander and Nikishina, Irina},
  booktitle = {Proceedings of the 62nd Conference of the Association for Computational Linguistics},
  month = aug,
  year = {2024},
  address = {Bangkok, Tailand},
  publisher = {Association for Computational Linguistics},
  dimensions = {false},
}

Are Large Language Models Good at Lexical Semantics? A Case of Taxonomy Learning

Viktor Moskvoretskii, Alexander Panchenko, and Irina Nikishina

In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), May 2024

Abs Bib

Recent studies on LLMs do not pay enough attention to linguistic and lexical semantic tasks, such as taxonomy learning. In this paper, we explore the capacities of Large Language Models featuring LLaMA-2 and Mistral for several Taxonomy-related tasks. We introduce a new methodology and algorithm for data collection via stochastic graph traversal leading to controllable data collection. Collected cases provide the ability to form nearly any type of graph operation. We test the collected dataset for learning taxonomy structure based on English WordNet and compare different input templates for fine-tuning LLMs. Moreover, we apply the fine-tuned models on such datasets on the downstream tasks achieving state-of-the-art results on the TexEval-2 dataset.
@inproceedings{moskvoretskii-etal-2024-large-language, title = {Are Large Language Models Good at Lexical Semantics? A Case of Taxonomy Learning}, author = {Moskvoretskii, Viktor and Panchenko, Alexander and Nikishina, Irina}, editor = {Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen}, booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)}, month = may, year = {2024}, address = {Torino, Italia}, publisher = {ELRA and ICCL}, url = {https://aclanthology.org/2024.lrec-main.133}, pages = {1498--1510}, dimensions = false, }

MLEM: Generative and Contrastive Learning as Distinct Modalities for Event Sequences

Viktor Moskvoretskii, Dmitry Osin, 5 more authors

arXiv preprint, 2024

arXiv Bib

@article{moskvoretskii2024mlem,
  title   = {MLEM: Generative and Contrastive Learning as Distinct Modalities for Event Sequences},
  author  = {Moskvoretskii, Viktor and Osin, Dmitry and Shvetsov, Egor and Udovichenko, Igor and Zhelnin, Maxim and Dukhovny, Andrey and Zhimerikina, Anna and Burnaev, Evgeny},
  journal = {arXiv preprint arXiv:2401.15935},
  year    = {2024},
  url     = {https://arxiv.org/abs/2401.15935}
}