Unverified50% confidenceFactExact time
Transformer架构由Google于2017年提出，Encoder负责将输入文本编码为高维向量表示，Decoder基于编码表示逐token生成输出文本
1
Sources
50%
Confidence
Long-term
Relevance
7/14/2026
First Seen
Sources
LangChain实战入门：RAG应用与智能体开发全景指南
Related Claims
UnverifiedThe 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 tasks78% similarVerifiedLarge language models are based on the Transformer architecture and are trained on massive amounts of text data.70% similarUnverifiedTransformer uses Positional Encoding to retain word order information while performing parallel computation.70% similarUnverifiedThe Transformer architecture generates text in an autoregressive manner, outputting one token at a time and appending it to the input sequence to predict the next token, resulting in a serial inference process68% similarUnverifiedPure text models are based on the Transformer architecture and only process tokenized text sequences, lacking the encoding pathway to convert pixel information into semantic representations.68% similar
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