What component of the Transformer model is responsible for generating the next token of an output sequence?

The Decoder component of the Transformer model is specifically designed to generate the next token of an output sequence. Unlike the Encoder, which processes and encodes the input data, the Decoder handles the sequential generation of output, taking into account previously generated tokens. This is crucial for tasks such as language modeling and text generation, where each token depends on the context provided by the preceding tokens.

The Decoder operates through a mechanism that combines attention and generation of words, allowing it to utilize the context derived from the entire sequence provided by the Encoder, along with previously generated tokens. This process enables the Decoder to produce coherent and contextually relevant output.

The other components, while essential to the overall architecture, do not fulfill this specific role. For instance, the Encoder is responsible for understanding and embedding the input sequence, while the Multi-Head Attention and Feed Forward Network are critical for processing information within both the Encoder and the Decoder but do not directly generate the output sequences.