Potential application of generative language models in modern media communication and journalism (on the example of Claude and Yalm)
DOI:
https://doi.org/10.47475/2070-0695-2024-53-3-108-121Keywords:
media communication, generative language models, artificial intelligence, contemporary journalismAbstract
This article serves as a theoretical and practical synthesis based on the results of an experiment conducted by the authors. Although the core of this publication is grounded in the relevant outcomes of the aforementioned authorial experiment, the work also presents a brief theoretical analytical review of the history and key problem areas in the development of generative language models at the current stage. The methodological foundation of this study lies in the systematic and structural-functional approaches to exploring the potential use of generative language models in contemporary journalism, complemented by a three-part case study that forms the basis of the work. A concise analytical overview of the current state of academic discourse related to the stated research topic is provided at the beginning of the article. The main focus is on the transformation of the media sphere as a whole, and media communication tools, through the lens of AI technology development. The primary objective of this work, as set by the authors, was to conduct a theoretical and applied investigation into the current potential of leading generative language models in the field of media communication and journalism, utilizing the aforementioned authorial experiment. The importance of such research is underscored by the unprecedented rapid development of large language models over the past two years. In the author’s experiment, the generation results of large language models (Claude 2.0 and Yandex GPT), developed and publicly available in 2023, were used and compared. The experiment was based on generating texts in various genres of contemporary journalism, which were subsequently evaluated both by the authors and by the automated system Glavred. Based on the experiment’s outcomes, the authors formulated conclusions and brief analytical predictive statements within the context of the stated topic.
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Copyright (c) 2024 Irina Zyryanova, Alexander Chernavsky
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