An approach to the formation of intellectual academic genealogy using large language models
Authors: Lerner I.M. , Marinosyan A.Kh. , Grigoriev S.G. , Yusupov A.R. , Anikyeva M.A. , Garifullina G.A. 
Published: October 2024

Today, in the era of transition to the information society, the issue of analysis and processing of scientific information is extremely acute. This is due to the fact that the use of results and processing directly and indirectly affect the country's strategy in the field of educational activities. Direct influence is manifested in the introduction of the results of scientific activity into the educational process in the form of educational material. At the same time, indirect influence involves a more complex mechanism, which was described by the authors earlier and has a more long-term effect due to the implementation of the effects of generational values that are formed at the age of 12–14 years under the influence of existing technological development, family values and the socio-cultural environment, which in turn forms patterns of behavior that affect the process of choosing and studying new information, developing new technical solutions, making key decisions, which directly influence the industrial potential of the country. Currently, there is a fairly large number of scientometric methods for studying scientific information, which, among other things, allow implementing in a limited form the idea of D. Price about the "invisible college", when in the course of analyzing scientific information it is possible to determine the scientific social structure consisting of universities, research institutes, scientific journals, conferences, scientists in individual fields of science. However, all methods use in one form or another formal analysis and a context-free approach to assessing citations, which does not allow for a qualitative assessment of the processing and transformation of information in the process of scientific activity, which is a necessary condition for the development of the country's industry. Creating an algorithm for classifying scientific information by generating prompts for a large language model to ensure contextual analysis of citations in scientific papers and classifying scientific information based on deep semantic analysis. Requirements have been formed for the selection of scientific information that provides the highest quality analysis results from the point of view of expert opinion. The presented algorithm for generating queries to a large language model facilities contextual analysis and classification of bibliographic references in scientific information. The proposed approach for clustering scientific information takes into account the multidisciplinary nature of research and ensures the continuity of research based on multidimensional bases. It is shown that the quality of contextual analysis of bibliographic references due to the developed algorithm has increased by 27% compared to using a large language model without this algorithm. Based on experimental studies, the possibility of predicting changes in the social sphere is shown. The algorithm for generating queries to a large language model is presented, facilities for contextual analysis and classification of bibliographic references in scientific information. The proposed approach for clustering scientific information takes into account the multidisciplinary nature of research and ensures the continuity of research based on linguistic multidimensional bases.