Decoder transformers are a crucial component of the transformer architecture, primarily used in tasks such as text generation. The decoder’s role is to convert the internal vector representations produced by the model into human-readable text. This process involves various decoding strategies, which can significantly influence the quality and characteristics of the generated output. Common methods include greedy search and beam search, among others ¹.

In the transformer architecture, the decoder consists of multiple layers, each incorporating mechanisms like masked multi-head attention and encoder-decoder multi-head attention. These mechanisms allow the decoder to focus on relevant parts of the input sequence while generating the output, ensuring that the context is preserved ³. The decoder processes the target sequence and the memory from the encoder, utilizing various masks to manage the flow of information ⁴.

Overall, the design and functionality of decoder transformers are essential for achieving high-quality results in natural language processing tasks.