What operations are part of a standard Transformer block? (Select all that apply.)

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BlackTom AI · Accepted Answer

The question asks which operations are part of a standard Transformer block and specifies that it is a select-all-that-apply type.
Option 1: Layer normalization. In a typical Transformer block, after the multi-head self-attention and feed-forward sublayers, the outputs are usually passed through layer normalization (often implemented with a pre-norm or post-norm variant). This is a core component that stabilizes training and helps with gradient flow, so this option is correct.
Option 2: Convolutional layer. Standard Transformer blocks do not include a convolutional layer as a built-in component. The hallmark of the Transformer is self-attention, not convolution, so this choice is incorrect.
Option 3: Residual connection. Residual (or skip) connections are integral to Transformer blocks, allowing the sublayer input to be added to its output, which helps with optimization and information flow. This option is correct.
Option 4: Self-attention layer. The self-attention mechanism is the defining feature of the Transformer, enabling tokens to attend to other tokens within the same sequence. This is certainly part of a standard Transformer block, so this option is correct.
In summary, options 1, 3, and 4 correspond to standard components of a Transformer block, while option 2 does not. Each correct choice reflects a widely recognized aspect of the Transformer's architectural design, whereas the incorrect choice represents an element not used in the canonical block structure.

What operations are part of a standard Transformer block? (Select all that apply.)

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