What is the primary function of a decoder in text generation tasks?

The primary function of a decoder in text generation tasks is to predict and output the next word in a sequence. In the context of models like transformers or sequence-to-sequence architectures, the decoder takes the encoded information from the input (often processed by an encoder) and generates text by making predictions at each step based on the input it has received and the previously generated outputs.

This word prediction process is essential for generating coherent and contextually appropriate text as it allows the model to produce a sequence of words that make linguistic sense. By focusing on the relationships between words and maintaining context, the decoder effectively contributes to the overall task of text generation, creating narratives or responses based on the learned relationships from the training data.

In contrast, the other options serve different functions that are not aligned with the primary role of a decoder. For example, creating visual representations of text relates more to data visualization rather than text generation, and encoding words into numerical data is a task typically performed by an encoder. Generating summary statistics of word frequency is more about analyzing text rather than generating new text. Thus, the function of the decoder is uniquely focused on the prediction and generation of subsequent words in a textual sequence.