Generative AI Fundamentals: High-Level Overview Transformer Model Elements

The correct answer is C. Encoder and decoder. This is a good explanation because a transformer model consists of two main components: an encoder and a decoder. The encoder processes the input data, such as text or speech, and generates a representation that captures its meaning and structure. The decoder uses this representation to generate the output data, such as a translation or a summary

Some of the other options are incorrect or incomplete because:

• A. Text and image are not elements of a transformer model, but rather types of data that can be processed by a transformer model. Transformer models can handle both text and image data, as well as other modalities, by using different types of embeddings and attention mechanisms.

• B. Supervised and unsupervised are not elements of a transformer model, but rather types of learning methods that can be used to train a transformer model. Supervised learning requires labeled data, while unsupervised learning does not. Transformer models can be trained using both methods, depending on the task and the availability of data.

• D. Generative and predictive are not elements of a transformer model, but rather types of tasks that can be performed by a transformer model. Generative tasks involve creating new content, such as text or images, based on some input or prompt. Predictive tasks involve making inferences or predictions, such as classification or regression, based on some input or query. Transformer models can perform both types of tasks, by using different architectures and objectives.