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Оценка сложности текстов по русскому как иностранному: инструментарий и алгоритм на основе больших языковых моделей
Актуальные проблемы филологии и педагогической лингвистики. 2026. № 2. С. 53–66.
Солнышкина М. И., Андреева М. И., Гайнутдинова Д. З.
Солнышкина М. И., Андреева М. И., Русистика, Россия 2026 Т. 24 № 1 С. 120–137
Растущий интерес к российскому образованию, культуре и науке в значительной степени определяет насущную потребность в разработке инструментария подбора учебных текстов по русскому языку как иностранному (РКИ). Цель исследования — разработать алгоритм и инструментарий оценки лексической сложности текста РКИ (по CEFR) с использованием больших языковых моделей (LLM). Исследование выполнено на материале обучающей выборки, включающей стандартизированные лексические ...
Added: October 19, 2026
Alexandrov D., Vasilevsky V., Rezunik L. et al., Труды Института системного программирования РАН 2026 Т. 38 № 5 С. 73–90
Large language models demonstrate limitations in tasks that require precise information retrieval from very long input sequences. This limitation affects code generation in large object-oriented systems, where correctness depends on complex inter-class dependencies and the analysis of long execution traces that exceed standard context window sizes. This paper presents JavaCapsule, a technology for automated generation ...
Added: October 12, 2026
Sherstinova T., Урих А. Е., Вестник Новосибирского государственного университета. Серия: Лингвистика и межкультурная коммуникация 2026 Т. 24 № 2 С. 42–54
Цифровые технологии XXI века способствуют формированию новых методологических подходов в гуманитарных науках, включая литературоведение и биографические исследования. Для автоматизации процессов сбора и аннотирования информации все чаще используют большие языковые модели (Large Language Models, LLM), включая ChatGPT. Развитие подобных инструментов стимулирует переход от традиционных методологических подходов, основанных на исключительно ручном аннотировании и экспертной верификации, к гибридным, ...
Added: September 28, 2026
Alexandrov D., Vasilevsky V., Rezunik L. et al., В кн.: Труды Института системного программирования РАНТ. 38. Вып. 5.: ИСП РАН, 2026. С. 73–90.
Ограничения при выполнении задач, требующих точного извлечения информации из сверхдлинных последовательностей, приводят к снижению качества генерации и отладки кода крупных объектно-ориентированных систем, характеризующихся сложными межклассовыми зависимостями и необходимостью анализа длинных трассировок выполнения, превышающих размеры стандартных контекстных окон. Предлагается технология JavaCapsule для генерации и отладки Java-кода, реализующая итеративную отладку на основе структурированной обратной связи, включающей результаты ...
Added: September 24, 2026
Sorokin I., Tekic Z., Technology in Society 2026 Vol. 88 Article 103456
Generative AI (GenAI) digital products challenge established new product development and product success frameworks because performance after launch remains variable: outputs differ across runs and contexts, behavior can shift under drift, and realized value depends on user interaction, oversight, and governance at scale. Focusing on large language model (LLM)-based GenAI digital products, this study identifies ...
Added: September 23, 2026
Filatova Asya, Didenko A., Ravedovskaya U., Journal of Teaching English for Specific and Academic Purposes 2025 Vol. 13 No. 3 P. 529–538
Emergence of generative artificial intelligence (GenAI) is revolutionizing teaching and evaluation in higher education. Early adopters have already demonstrated how large language model (LLM) conversational agents can serve as on-demand tutors, yet empirical evidence regarding their effectiveness in facilitating conceptual learning in non-computational domains is scant. Based on constructivist learning theory, this paper presents a ...
Added: September 18, 2026
Shipovalova L., Asya A. Filatova, Социология власти 2026 Vol. 38 No. 1 P. 224–243
This article examines the integration of large language model based chatbots into educational practices in Russian higher education through the lens of critical theory and curriculum ideologies. Drawing on Ivan Illich’s concept of convivial tools and Jürgen Habermas’s distinction between instrumental and communicative rationality, the authors conceptualize large language models as technologies capable of operating ...
Added: September 18, 2026
Karpenko I., Человек 2026 Т. 37 № 5 С. 42–58
This article examines the question of agency in artificial intelligence systems. A distinction is proposed between three types of agency: ontological, functional, and ascribed. The central thesis is that functional agency is a genuine rather than metaphorical type, which is grounded through the teleological theory of function and through a critique of the argument from ...
Added: September 16, 2026
Rezunik L., Alexandrov D., Mikhail Prozorskiy, , in: Intelligent Decision Technologies. Proceedings of the 17th KES-IDT 2025 ConferenceVol. 450.: Cham: Springer, 2026. P. 81–91.
Multi-Agent Systems (MAS) can benefit from Large Language Models (LLMs), but hallucinations pose risks to decision-making. This paper introduces an approach for creating MAS based on LLMs and proposes a generalized architecture for such systems. We ensure that reasoning is conducted through predicate logic to minimize errors, and LLMs are exclusively utilized to translate natural ...
Added: September 14, 2026
Egorova I. S., Journal of Employment and Career 2025 Vol. 4 No. 4 P. 27–43
Background. The rapid diffusion of large language models has intensified claims that professional translation is approaching technological redundancy. The available evidence, however, is dispersed across labour economics, computational linguistics, translation stu-dies, industry research, and translator education, and it does not support a uniform conclu-sion for the profession as a whole.Purpose. This article examines how artificial ...
Added: September 8, 2026
Джонов А. Т., Avdoshin S. M., Информационные технологии 2026 Т. 32 № 8 С. 421–427
This systematic review presents an analysis of the "Vibe Coding" methodology — a contemporary approach to the iterative software development process using Large Language Models (LLMs). Code generation tools are transforming software development by enabling programmers to formulate tasks and describe the desired behavior of software in natural language, while LLMs generate source code corresponding ...
Added: August 25, 2026
Rozhkova K., Roshchin S., Roshchina Y., Вопросы экономики 2026 № 8 С. 29–54
Стремительное развитие генеративного искусственного интеллекта (генИИ) усилило дискуссии о том, как новые технологии трансформируют рынок труда, особенно позиции и задачи, которые еще недавно мог выполнять только человек. Эксперименты показывают, что генеративный ИИ может обеспечить значительный прирост производительности, особенно менее квалифицированных сотрудников, что может приводить к возникновению индивидуальной зарплатной отдачи на рынке труда. Впервые для России ...
Added: August 11, 2026
Surkov A., Ignatenko V., Koltsov S., Computers, Materials and Continua 2026 Vol. 88 No. 3 Article 74
Large language models have recently demonstrated promising capabilities in mathematical reasoning; however, their performance on tasks requiring strict symbolic manipulation, such as solving differential equations, remains limited, especially for compact models. In this work, we investigate whether activation steering combined with reinforcement learning can improve the quality of solutions generated by pretrained language models without ...
Added: July 8, 2026
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
Tyuryakova-Matveeva D., Цифровые гуманитарные исследования 2026 № 1 С. 4–26
The article explores cultural references in the works of Andrei Voznesensky by analyzing the personalities he mentions. A total of 1,678 works were processed, including poetry, prose, and early unpublished poems. NER methods based on Natasha, spaCy, and LLM Grok tools made it possible to study the frequency of mentions of famous people and their ...
Added: May 31, 2026
Подболотова М. И., Адамский А. И., Kolachev N. et al., Высшее образование в России 2026 Т. 35 № 4 С. 21–35
The purpose of the article is to present and justify a pedagogical model of personal
ized feedback based on large language models (LLM) for the educational process in a human
ities-oriented master’s program. The relevance of the study is determined by the objectives of
digital transformation of higher education in the Russian Federation, outlined in Presidential
Decree No. 474 ...
Added: May 4, 2026
Baysha O., Trofimov V., Российская школа связей с общественностью 2026 № 40 С. 171–191
A growing number of scholars are warning about the dangers of the reproduction by generative AI of socio-political and ideological biases absorbed by models from the texts on which they were trained. If a given model was trained on Western media texts, it may generate narratives that reproduce West centric views of world events. This ...
Added: April 21, 2026
Seredkina E., Seletkova G., Mikhailovsky A., Technology and Language 2026 Vol. 7 No. 1 P. 63–79
The rapid diffusion of Large Language Models (LLMs) into socially and politically sensitive domains raises critical questions about the nature and origins of political bias in artificial intelligence. While existing research often treats bias as a technical flaw to be minimized, this article advances a broader philosophical and cultural interpretation of LLM bias as an ...
Added: April 1, 2026