Comparative Analysis of Generative AI Models

Abstract

Generative AI models have the potential to create a variety of new content based on training data. They are not only able to create textual content but also other multimedia content such as images, audio, video, etc. They have gained popularity in recent years as they have a major impact on various fields. They are used for several applications from text generation, image generation, and music composition to education, healthcare, and metaverse. Still, several challenges are faced while developing and applying these models i.e., trustworthiness, biased content, overfitting and regulatory concerns. In this paper, the comparative analysis of various generative AI models concerning different parameters is performed with respect to tools, frameworks, input, output, development authority, etc. In addition to these, Applications of different generative AI Models are discussed in various domains

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