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June 5, 2026
HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.
June 4, 2026
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.
June 3, 2026
Pocket Money, Personal Interest, and Family Practices: What Shapes Students Economic Literacy?
University students' economic literacy depends not only on their field of study but also on their interest in economics, the learning environment, and family financial practices. For example, students who received pocket money irregularly tend to perform better on economic literacy tests than their peers who received financial support on a regular basis. These findings come from a study conducted by HSE University involving more than 1,100 students from five Russian universities. The findings have been published in Cakrawala Pendidikan.

 

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К происхождению эвиденциальности в нахско-дагестанских языках: структурные и ареальные перспективы

Вестник Православного Свято-Тихоновского гуманитарного университета. Серия 3: Филология. 2018. № 57. С. 110–123.
Verhees S.

The paper considers the grammatical expression of information source with past tense forms of the verb in the Nakh-Daghestanian languages. These languages are spoken on a relatively compact territory in the North Caucasus and, partly, in the Transcaucasian area. The area is part of a larger area ranging from the Balkan Peninsula to Central Asia, which includes the Caucasus, and where similar verb forms used to express information source are found. It is considered plausible that these forms arose as the result of language contact with Turkic, and for some languages (e.g. Armenian, Georgian), this is confirmed. The paper compares the characteristics of these forms in the Nakh-Daghestanian language family based on descriptive grammars, and illustrates their genetic and areal distribution on a map. I will show that the areal vs. genetic distribution is not trivial. There are three distinct zones within the territory of the Nakh-Daghestanian languages: more grammaticalized forms are attested in the nortwestern region, partly grammaticalized forms are dominant in the central region, and in the southern area the feature is absent. It is currently impossible to establish how these forms appeared in the Nakh-Daghestanian languages, through contact with which Turkic language exactly, and how this process took place. However, the distribution outlined in this article indicates that language contact played a role in their dissemination.

Priority areas: humanitarian
Language: Russian
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Keywords: морфологияmorphologyграмматикализацияperfectперфектэвиденциальностьverbal morphologyглагольная морфологияязыковой контактнахско-дагестанские языкикавказские языкиTurkic languagesтюркские языкиgrammaticalizationEast Caucasian languagesCaucasian languagesevidentialityNakh-Daghestanian languageslanguage contactareal typologyареальная типология
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