Unverified60% confidenceFactExact time
Mainstream LLMs are based on the Transformer architecture and acquire capabilities through self-supervised learning of next-token prediction on massive text data.
2
Sources
60%
Confidence
Long-term
Relevance
7/17/2026
First Seen
Sources
Related Claims
VerifiedLLMs generate text in an autoregressive manner based on the Transformer architecture, predicting only one token per forward pass and then appending that token to the input sequence before predicting the next token.81% similarUnverifiedLLM的预训练本质是在海量文本上进行下一词预测（Next Token Prediction），通过Transformer架构的注意力机制建立词语间语义关联81% similarUnverifiedModern LLMs typically contain billions to hundreds of billions of parameters and are pre-trained on massive amounts of text based on the Transformer architecture.78% similarUnverifiedTransformer-based LLMs perform an independent forward-pass computation for each inference, with model weights fixed during the inference phase; what is referred to as context memory is essentially the re-injection of historical conversation text into the prompt.77% similarVerified当前主流大语言模型本质上是基于Transformer架构的概率预测系统，通过预测下一个最可能的Token来生成内容77% similar
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