Anthropomorphic metaphors as a mechanism for humanizing artificial intelligence in generative texts
https://doi.org/10.18384/2949-5075-2025-4-54-61
Abstract
Aim. This article explores linguistic epistemology in the context of text generation by neural network models based on artificial intelligence.
Methodology. The study analyzes the artificial text of the project “Autobiography of a Neural Network” (2023) using content analysis and a linguo-cognitive approach involving quantitative metaphor analysis and conceptual schema modeling.
Results. The research shows that the linguistic epistemology of generation is defined by a principle that combines the functions of a neural network algorithm with the cognitive settings of the subject. Conceptual metaphors of cognitive functions “humanize” the generative agent, allowing it to imitate cognitive processes and create new forms of knowledge representation based on the analysis of training texts.
Research implications. Generated texts construct a simulation of epistemic processes through systematic metaphors and anthropomorphization strategies. The research findings can be applied to AI interface design and UX enhancement, critical discourse analysis of manipulation strategies in text generation processes.
About the Author
S. V. Ostapenko
Altai State University
Russian Federation
Russian Federation
Svetlana V. Ostapenko (Barnaul) – External Postgraduate Student, Department of Media Communications, Advertising Technologies and Public Relations
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