Unverified50% confidenceFactExact time
The 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.
1
Sources
50%
Confidence
Long-term
Relevance
10/22/2026
First Seen
Sources
Carbon-X: A 14.8B Open-Source Model That Handles DNA and Text Simultaneously
redditr/MachineLearning10/22/2026
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Unverified大语言模型通常采用仅解码器（Decoder-only）的Transformer变体，通过因果掩码确保每个位置只能关注其之前的token78% similarUnverifiedTransformer inference is divided into two stages, prefill and decode: prefill processes the entire input prompt in parallel at once and is mainly compute-bound; decode generates output tokens one by one and is mainly memory-bandwidth-bound.76% similarUnverifiedThe 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.74% similarVerifiedThe original Transformer consists of an Encoder and a Decoder; BERT-type models use only the Encoder and excel at understanding and classification, while GPT-type models use only the Decoder and focus on text generation.74% similarVerifiedThe Transformer's encoder is responsible for mapping the input sequence to a contextual representation and is suitable for classification and understanding tasks, while the decoder autoregressively generates the output sequence and is suitable for text generation tasks72% similar
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