SYNERGY OF INFORMATION TECHNOLOGIES AND NEURAL NETWORKS FOR TEXT CONTENT GENERATION
Oksana Berezhna · Scientific notes of Taurida National V I Vernadsky University Series Technical Sciences · 2024
The article discusses the potential of neural networks in creating textual content, highlighting their capabilities, limitations, and future directions of use.Neural networks are able to generate adaptive and personalized content that meets the specific requirements and preferences of users.However, the main challenges remain in ensuring the quality, relevance, and originality of automatically generated texts, which is critical for their practical application.Combining information technology with the capabilities of neural networks opens up prospects for improving these processes, but there is still a need for a deeper study of the optimization of such systems.The relevance of the study is due to the possible impact of these solutions on various aspects of public life and the cultural environment.The paper provides a comparative analysis of the features of using recurrent neural networks, variational autoencoders, generative adversarial networks, and transformers in the creation of textual content, and explores their advantages and limitations in application.Possible difficulties in using generative adversarial networks to create textual and media content (photos, drawings, animations) and the reasons for generating content with unrealistic content are discussed in more detail.The author proposes criteria for creating a comprehensive assessment of the quality of generated content that can be adapted to specific tasks or requirements.The results of the study include a comprehensive review of the capabilities and limitations of different types of neural networks in creating textual content; examples of their application in various fields such as writing, marketing and business; limitations of using neural networks to create textual content in creative writing.The author emphasizes the need for careful consideration of ethical aspects and the development of recommendations and standards for the use of neural networks to generate a variety of content.