👉 Receivers, also known as decoders in the context of transformer models, are crucial components responsible for interpreting the encoded representations generated by the encoder layers. They take the output of the encoder, which is a sequence of vectors capturing the semantic and contextual information from the input data, and process this information to produce a coherent output. This decoding process involves predicting the next token in a sequence, such as a word in a sentence, by leveraging the relationships and dependencies encoded in the input through the transformer architecture. Receivers utilize mechanisms like self-attention to weigh the importance of different parts of the input sequence, enabling them to generate contextually relevant and coherent outputs. In essence, receivers bridge the gap between the high-level understanding provided by the encoder and the practical generation of text or other sequential data, making them indispensable for tasks like machine translation, text summarization, and language modeling.
