🤯 Did You Know (click to read)
The decoder's cross-attention layers allow it to focus on relevant parts of the input sequence when generating each output token.
The encoder processes the input sequence through layers of self-attention and feed-forward networks to produce embeddings. The decoder attends to encoder outputs via cross-attention layers while generating output tokens autoregressively. This structure allows effective sequence-to-sequence learning for machine translation, summarization, and question-answering tasks.
💥 Impact (click to read)
Encoder-decoder Transformers improve performance on translation and generative tasks by explicitly modeling input-output dependencies.
Understanding this architecture helps researchers implement Transformer-based models in NLP applications and adapt them for multimodal or structured data.
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