Which component of an encoder-decoder model is responsible for sequence generation?

In an encoder-decoder model, the decoder component is responsible for generating sequences. This model architecture is commonly used in tasks such as machine translation, where input data (like a sentence in one language) needs to be transformed into output data (the corresponding sentence in another language).

The encoder processes the input sequence and compresses the information into a context vector, which captures the relevant features of the input. This context vector is then passed to the decoder, which uses it as the starting point to generate the output sequence token by token. The decoder utilizes both the context from the encoder and its previously generated tokens to predict the next token in the sequence until it reaches an end-of-sequence signal or a predefined length.

Thus, the decoder's role in generating sequences is central to the functionality of the encoder-decoder framework.