Attention in Transformer Blocks: Encoder and Decoder
To understand transformers, you need to know how attention works inside them. A transformer has two main parts: the encoder and the decoder. Each part is made up of several identical blocks. Attention mechanisms are a key feature in both parts.
In the encoder, each block uses self-attention. This means every position in the input sequence can focus on any other position, helping the model capture all possible relationships in the input.
The decoder is a bit more complex. Its blocks have both self-attention layers and cross-attention layers. The decoder's self-attention only looks at earlier positions in the output (using masking). The cross-attention layer lets each position in the decoder use information from every position in the encoder's output. This setup helps the decoder combine what it has already generated with the full context from the input sequence.
Self-attention allows a sequence to attend to itself, capturing dependencies within the same sequence. Cross-attention, by contrast, enables one sequence (such as the decoder's current state) to attend to another sequence (such as the encoder's output), facilitating information exchange between the encoder and decoder in transformers.
This design, with attention mechanisms embedded throughout both encoder and decoder blocks, enables highly flexible information flow.
In the encoder:
- Self-attention allows every token to directly access and integrate information from all other tokens in the input sequence;
- This helps you capture both short-range and long-range dependencies, so the model understands context and relationships no matter where they appear in the input.
In the decoder, attention mechanisms are layered for even greater flexibility:
- Masked self-attention ensures that each token in the output sequence can only attend to earlier tokens, preventing the model from "seeing the future" and maintaining proper sequence generation;
- Cross-attention connects the decoder to the encoder's output, letting each output token gather relevant details from the entire encoded input.
This combination means that, at every step, the decoder can use both what it has already generated and the full context of the input sequence.
This flexible, context-aware approach is what makes transformers so powerful for tasks like language modeling, translation, and many other applications where understanding and generating sequences is essential.
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Can you explain how self-attention actually works in detail?
What are some real-world examples of tasks where transformers outperform other models?
How does masking in the decoder's self-attention prevent information leakage?
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Completion rate improved to 10Attention in Transformer Blocks: Encoder and Decoder
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To understand transformers, you need to know how attention works inside them. A transformer has two main parts: the encoder and the decoder. Each part is made up of several identical blocks. Attention mechanisms are a key feature in both parts.
In the encoder, each block uses self-attention. This means every position in the input sequence can focus on any other position, helping the model capture all possible relationships in the input.
The decoder is a bit more complex. Its blocks have both self-attention layers and cross-attention layers. The decoder's self-attention only looks at earlier positions in the output (using masking). The cross-attention layer lets each position in the decoder use information from every position in the encoder's output. This setup helps the decoder combine what it has already generated with the full context from the input sequence.
Self-attention allows a sequence to attend to itself, capturing dependencies within the same sequence. Cross-attention, by contrast, enables one sequence (such as the decoder's current state) to attend to another sequence (such as the encoder's output), facilitating information exchange between the encoder and decoder in transformers.
This design, with attention mechanisms embedded throughout both encoder and decoder blocks, enables highly flexible information flow.
In the encoder:
- Self-attention allows every token to directly access and integrate information from all other tokens in the input sequence;
- This helps you capture both short-range and long-range dependencies, so the model understands context and relationships no matter where they appear in the input.
In the decoder, attention mechanisms are layered for even greater flexibility:
- Masked self-attention ensures that each token in the output sequence can only attend to earlier tokens, preventing the model from "seeing the future" and maintaining proper sequence generation;
- Cross-attention connects the decoder to the encoder's output, letting each output token gather relevant details from the entire encoded input.
This combination means that, at every step, the decoder can use both what it has already generated and the full context of the input sequence.
This flexible, context-aware approach is what makes transformers so powerful for tasks like language modeling, translation, and many other applications where understanding and generating sequences is essential.
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