Verified80% confidenceFactTime unknown
LLMs 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.
7
Sources
80%
Confidence
Long-term
Relevance
5/31/2026
First Seen
Sources
LangChain流式输出与异步实战：Agent场景完整实现
youtubeJames Briggs
Related Entities
Related Claims
UnverifiedMainstream LLMs are based on the Transformer architecture and acquire capabilities through self-supervised learning of next-token prediction on massive text data.81% 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.79% similarUnverifiedLLMs generate text in an autoregressive manner, predicting the next token one at a time, with each prediction based on all prior context.77% similarUnverifiedThe autoregressive generation characteristic of LLMs determines that their output is streamed token by token, stemming from the decoding strategy of the Transformer architecture.76% similarUnverifiedLLMs generate text in an autoregressive manner, producing only one token at a time, and the generation of the next token must wait for the previous token to be completed.76% similar
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