Unverified60% confidenceFactExact time
The decoder-only architecture uses a causal attention mask to ensure each token can only attend to its preceding context, making it suitable for autoregressive text generation tasks.
2
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60%
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Long-term
Relevance
8/24/2026
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Related Claims
UnverifiedThe decoder adds two mechanisms on top of the encoder: masked self-attention and cross-attention; masked self-attention ensures that during generation only previously produced tokens can be seen, while cross-attention allows the decoder to query the encoder's output.75% similarUnverifiedThe decoder-only architecture uses only the Transformer decoder stack, predicting the next token one by one in an autoregressive manner through a causal attention mask, learning during training to predict subsequent content based on preceding context.74% similarUnverifiedThrough component memoization and state splitting, the rendering scope on each token arrival can be compressed from the entire message list down to a single component.73% similarUnverifiedIn-context conditioning concatenates the text condition and image tokens into the same sequence, eliminating the cross-attention module, but it lengthens the sequence and increases the amount of attention computation.72% similarUnverifiedTo avoid specific characters at the decoding stage, one needs to build a token-to-character mapping and dynamically mask token candidates containing the target character during generation.72% similar
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