DOI 10.17586/0021-3454-2024-67-11-958-968
UDC 004.912: 004.822
ANALYSIS OF STATISTICAL CHARACTERISTICS OF ARTIFICIALLY GENERATED TEXTS
St. Petersburg Institute for Informatics and Automation of Russian Academy of Sciences (SPIIRAS), Laboratory of Research Activities Automation;
A. A. Zaytseva
St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, Laboratory of Research Automation ; Senior Scientist
A. Y. Aksenov
St. Petersburg Federal Research Center of the RAS, St. Petersburg Institute for Informatics and Automation of the RAS, Research Automation Laboratory ; Senior Researcher
Reference for citation: Kuleshov S. V., Zaytseva A. A., Aksenov A. Yu. Analysis of statistical characteristics of artificially generated texts . Journal of Instrument Engineering. 2024. Vol. 67, N 11. P. 958–968 (in Russian). DOI: 10.17586/0021-3454-2024- 67-11-958-968.
Abstract. A new trend is considered, namely, the formation of content using artificial intelligence tools and technologies. Active implementation of artificial intelligence technologies for data generation leads to an increase in the share of artificially generated data that must be identified automatically to prevent errors (unreliability, misleading). Approaches to identifying text data created using neural network technologies are proposed, including heuristic rules based on the criterion of dependence of the abstract volume on the abstracting threshold, which allows for automatic evaluation of text documents in monitoring and search systems when processing large volumes of unstructured data. The obtained results lay the technological basis for the implementation of a wide range of practical solutions to ensure intellectual support for the collective behavior of participants in human-machine communities through the development of theoretical and technological foundations for processing unstructured data.
Abstract. A new trend is considered, namely, the formation of content using artificial intelligence tools and technologies. Active implementation of artificial intelligence technologies for data generation leads to an increase in the share of artificially generated data that must be identified automatically to prevent errors (unreliability, misleading). Approaches to identifying text data created using neural network technologies are proposed, including heuristic rules based on the criterion of dependence of the abstract volume on the abstracting threshold, which allows for automatic evaluation of text documents in monitoring and search systems when processing large volumes of unstructured data. The obtained results lay the technological basis for the implementation of a wide range of practical solutions to ensure intellectual support for the collective behavior of participants in human-machine communities through the development of theoretical and technological foundations for processing unstructured data.
Keywords: internet documents, artificial neural networks, large language model, Internet resources, artificial intelligence methods, data generation
Acknowledgement: the work was carried out with the support of the State assignment for 2024 No. FFZF-2022-0005.
References:
Acknowledgement: the work was carried out with the support of the State assignment for 2024 No. FFZF-2022-0005.
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