Which model is commonly used for text generation in generative AI?

The Generative Pre-trained Transformer (GPT) is widely utilized for text generation in generative AI due to its architecture and training methodology that effectively captures language patterns and context. GPT is based on the transformer model, which employs self-attention mechanisms that allow it to understand and generate coherent text by predicting the next word in a sequence based on the context of the preceding words. This capability enables it to produce text that is not only grammatically correct but also contextually relevant.

The training process involves unsupervised learning on a large corpus of text, allowing the model to learn various nuances of language, such as syntax, semantics, and even stylistic elements. This makes GPT particularly adept at tasks ranging from simple text completion to more complex forms of creative writing and conversational agents.

In contrast, while Long Short Term Memory (LSTM) networks are also used for sequential data and can generate text, they don't match the efficiency and performance of transformer-based models, especially in handling long-range dependencies in text. Convolutional Neural Networks (CNNs) are typically recognized for image processing tasks and aren't inherently designed for text generation. Similarly, Radial Basis Function Networks (RBFN) are more aligned with function approximation and are not normally utilized for text generation purposes