On the Applicability of Neural Networks in the Publishing Industry
Main Article Content
Abstract
The paper assesses the limits of applicability of large language models in editorial tasks within the publishing process and identifies the optimal format of interaction between humans and algorithmic systems.
The methodological basis of the study is a comparative experiment in which several popular neural network models — Alice AI, GigaChat, DeepSeek, Gemini, and ChatGPT — performed a statistical analysis of a control text in Russian. The quantitative characteristics of the text were determined: the number of words, characters with and without spaces, and the number of paragraphs. The obtained results were compared with reference values established using the MS Word text editor, which applies a deterministic character-counting algorithm.
The results of the experiment showed that neural network models demonstrate varying degrees of accuracy when performing tasks of quantitative text analysis. The main reason for such errors lies in the architecture of large language models and the use of tokenization algorithms, which break the direct connection between characters and the model’s internal representation of the text.
Based on the results obtained, the paper proposes the concept of a hybrid architecture for publishing information systems, in which generative language models are used to perform creative and analytical tasks, while operations requiring strict formal accuracy are assigned to specialized deterministic microservices. The proposed approach makes it possible to improve the reliability and predictability of intelligent publishing systems.
Article Details
References
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