Using artificial intelligence speech models to generate social media posts

Abstract

The article examines the use of artificial intelligence speech models for generating posts in social networks. Artificial intelligence (AI) is becoming increasingly integrated into our daily lives, and language models such as OpenAI's GPT-4 play a key role in this. Language models can be used for various tasks: automated translation, creating chatbots, and generating texts that match the style of a particular author or genre, formation personalized recommendations and even for detecting fake news. The article discusses how these models can be applied to generate content in social networks that is useful for personal blogs, commercial accounts, and news channels. Existing language models such as GPT, BERT, Gemini, and LLaMA were analyzed and their advantages and disadvantages were presented. The principle of operation of generative models and counting tokens when creating requests for artificial intelligence are considered, and relevant examples are given. The article describes the process of collecting and preparing data, filtering and optimizing it to reduce the cost of queries when training a model that provides the ability to generate content that corresponds to a given style and topic. The process of creating your own GPT application using the downloaded and processed dataset is described in detail. Comparative statistics of publications written by humans and artificial intelligence in the form of a table and graph are presented, and the results obtained are analyzed. In the future, the created GPT application will be able to integrate its own API services and use the DALL-E image generation model. This work highlights the importance and relevance of research in the field of AI language models and their application to generate content in social networks, opening up new opportunities for automating and optimizing these processes.

Keywords

• token
• language model
• artificial intelligence