?
RuCLEVR: A Russian Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
Communications in Computer and Information Science. 2024. Vol. 2364 CCIS. P. 109 – 121.
Biryukova K., Chelnokova D., Erkenova J., Tikhonova M.
Kopylova E.V., Tsegoeva O. G., Berlin V.A. et al., , in: Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной международной конференции «Диалог». Выпуск 24Issue 24.: M.: Max press, 2026. Ch. 26 P. 1–10.
This paper investigates how large language models correct complex grammatical errors in Russian academic
learner writing. Unlike traditional minimal-edit GEC systems, LLMs often apply generative rewriting strategies that
may improve fluency, but risk structural overcorrection and semantic drift. We introduce a new expert benchmark
derived from an authentic 3,1M-word learner corpus and construct an evaluation set annotated for ...
Added: June 27, 2026
Balagansky N., Maximov I., Gavrilov D., , in: Proceedings of the 13th International Conference on Learning Representations (ICLR 2025).: ICLR, 2025. P. 57940–57957.
Understanding how features evolve across layers in deep neural networks is a fundamental challenge in mechanistic interpretability, particularly due to polysemanticity and feature superposition. While Sparse Autoencoders (SAEs) have been used to extract interpretable features from individual layers, aligning these features across layers has remained an open problem. In this paper, we introduce SAE Match, ...
Added: February 25, 2026
Balakina Y. V., Grigoreva M., Соколова Е. Н., Вестник Российского фонда фундаментальных исследований. Гуманитарные и общественные науки 2025 Т. 123 № 4 С. 56–69
The article examines the potential of large language models (LLMs) for automated analysis of value-laden and patriotic discourse in Russian-language social media. Using a corpus of posts from VK, Odnoklassniki and Telegram (2023–2025), it investigates the extent to which automatic coding results align with expert annotation based on a specially developed categorical scheme. The codebook ...
Added: November 26, 2025
Chepikov I., Karpov I., , in: 26th International Conference, AIED 2025, Palermo, Italy, July 22–26, 2025, Proceedings, Part I. Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium, Blue Sky, and WideAIED.: Springer, 2025. P. 352 – 358.
Modern LLM models such as BERT, ChatGPT, DeepSeek have shown great potential in solving various tasks, including text classification, text generation, analysis and summary of documents. In this paper, we show that these models close to classical ML approaches based on decision trees not only in text processing, but also in processing classical tabular data ...
Added: September 4, 2025
Громенко Е. С., Калачева Д. С., Klokova K. et al., , in: Компьютерная лингвистика и интеллектуальные технологии: по материалам ежегодной международной конференции «Диалог» (2025).: [б.и.], 2025.
This study addresses the gap in evaluating large language models' (LLMs) cultural awareness and alignment within the Russian sociocultural context by introducing a structured framework comprising 8 Cultural Types (e.g., Spiritual Practitioner, Soviet Intellectual) and 5 catchphrase groups (e.g., memes, proverbs). A 400-question evalua tion dataset was developed to probe 10 multilingual LLMs, including GPT-4o, ...
Added: May 10, 2025
Morozov D., Garipov T., Lyashevskaya O. et al., Journal of Language and Education 2024 Vol. 10 No. 4 P. 71–84
Introduction: Numerous algorithms have been proposed for the task of automatic morpheme segmentation of Russian words. Due to the differences in task formulation and datasets utilized, comparing the quality of these algorithms is challenging. It is unclear whether the errors in the models are due to the ineffectiveness of algorithms themselves or to errors and inconsistencies ...
Added: January 7, 2025
Latipov I., Andrey Borevskiy, Kertesz-Farkas A., , in: Proceedings Volume 13169. Fifth International Conference On Computer Vision And Computational Intelligence (CVCI 2024) 29-31 January 2024, Bangkok, Thailand.: SPIE, 2024. Ch. 1316909.
Deep learning-based machine reasoning and visual question answering models achieve a near-human performance on their respective datasets; however, their performance dramatically drops under domain shift suggesting that models fail to generalize to the level of human-like reasoning. In this paper we present a new CLEVR-like dataset consisting of images-question pairs to evaluate the visual reasoning ...
Added: November 21, 2024
Russo M., Pavone P., Meissner D. et al., Quality and Quantity 2025 Vol. 59 No. Suppl 1 P. S343–S367
In OECD countries, Science, Technology and Innovation (STI) policies were seen as key aspects of coping with the Covid-19 pandemic. Now that the pandemic is over, identifying which policy mix portfolios characterised countries in terms of their non-Covid-19 related and Covid-19 specific STI policies fills a knowledge gap on changes in STI policies induced by ...
Added: September 27, 2024
Galitsky B., Chernyavskiy A., Ilvovsky D., , in: SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval.: Association for Computing Machinery (ACM), 2024. P. 2817–2821.
Large Language Models (LLM) often produce text with incorrect facts and hallucinations. To address this issue, we developed a fact-checking system Truth-O-Meter which verifies LLM results on the Internet and other sources of information to detect wrong claims/facts and proposes corrections for them. NLP and reasoning techniques such as Abstract Meaning Representation and syntactic alignment are ...
Added: May 9, 2024
Parameter-Efficient Tuning of Transformer Models for Anglicism Detection and Substitution in Russian
Daniil Lukichev, Kryanina Darya, Anastasia Bystrova et al., , in: Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной международной конференции «Диалог». Вып. 22.Вып. 22.: [б.и.], 2023. P. 295–306.
Added: April 25, 2024
Sergei O. Kuznetsov, Parakal E. G., Lecture Notes in Networks and Systems 2023 Vol. 776 P. 423–434
Inherently explainable Machine Learning (ML) models are able to provide explanations for their predictions by virtue of their construction. The explanations of a ML model are more comprehensible if they are expressed in terms of its input features. Our paper proposes an inherently explainable pipeline for document classification using pattern structures and Abstract Meaning Representation ...
Added: February 5, 2024
Switzerland: Springer, 2024.
This book constitutes revised papers from the International Workshops held at the 21st International Conference on Business Process Management, BPM 2023, in Utrecht, The Netherlands, during September 2023.
Papers from the following workshops are included:
• 7th International Workshop on Artificial Intelligence for Business Process Management (AI4BPM 2023)
• 7th International Workshop on Business Processes Meet Internet-of-Things (BP-Meet-IoT ...
Added: January 17, 2024
Северина Е. М., Ларионова М. Ч., Litera 2023 № 10 С. 211–222
The article considers a model of preparation of machine-readable (semantic) markup of texts for the Chekhov Digital project on the example of philological interpretation of individual significant elements of A. P. Chekhov's story "Death of an Official" and presentation of this information explicitly based on the standards of digital publication Text Encoding Initiative (TEI/XML). Based ...
Added: January 12, 2024
Кругликова В. Г., В кн.: Анализ речи: теоретические и прикладные аспекты: сборник научных статей.: [б.и.], 2023.
The article presents a comparative analysis of various language models used to generate texts and evaluates their effectiveness for the task of generating conversational speech. There are such models as GPT-3, BERT, LSTM involved in the comparative analysis. This study is part of a project of developing a system for generating dialogues in Russian. The ...
Added: December 10, 2023
Baklanova V., Kurkin A., Teplova T., China Finance Review International 2024 Vol. 14 No. 3 P. 522–548
Purpose – The primary objective of this research is to provide a precise interpretation of the constructed
machine learning model and produce definitive summaries that can evaluate the influence of investor sentiment on the overall sales of non-fungible token (NFT) assets. To achieve this objective, the NFT hype
index was constructed as well as several approaches of ...
Added: December 10, 2023
Kirina M., Человек: образ и сущность. Гуманитарные аспекты 2024 № 2(58) С. 176–204
The article focuses on the application of opinion mining techniques to evaluate user experience on the Hyperskill educational platform, using Python, Java, and Kotlin programming projects as the basis of analysis. The study utilizes sentiment analysis and keyword extraction methods to gauge users' attitudes towards the platform, learning process, and topics covered. To achieve this, ...
Added: December 9, 2023
Bolshakova E. I., Семак В. В., Интеллектуальные системы. Теория и приложения 2021 Т. 25 № 4 С. 239–242
An approach to automatic extraction of terms from an individual scientific text is reported, which combines known methods: linguistic patterns, statistical terminological measures, methods of graph ranking. The combined methods and stages for extracting, selection and ranking of terms are described, which are implemented for processing documents in Russian. The results of experiments on extracting ...
Added: November 23, 2023
Galitsky B., Ilvovsky D., Goncharova E., , in: Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной международной конференции «Диалог». Вып. 22.Вып. 22.: [б.и.], 2023.
We extend the concept of a discourse tree (DT) in the discourse representation of text towards data of various forms and natures. The communicative DT to include speech act theory, extended DT to ascend to the level of multiple documents, entity DT to track how discourse covers various entities were defined previously in computational linguistics, we now proceed ...
Added: November 10, 2023
I. K. Kusakin, Fedorets O. V., A. Y. Romanov, Scientific and Technical Information Processing 2023 Vol. 50 No. 3 P. 176–183
This paper discusses modern approaches to natural language processing and the application of machine learning models to the task of classifying short scientific texts in Russian. This study is devoted to the analysis of methods for vectorization of textual information, selection of a model for scientific paper clas- sification, and training of linguistic model BERT ...
Added: November 4, 2023