Unverified50% confidenceFactExact time
Autoregressive generation is the core decoding paradigm of current mainstream large language models (such as the GPT series): generating one token at a time and appending it to the end of the sequence, looping until an end token is encountered.
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50%
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Long-term
Relevance
9/15/2026
First Seen
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Related Claims
UnverifiedGPT系列底层架构基于自回归语言模型，通过逐Token预测的方式生成文本77% similarUnverified主流大语言模型（如GPT系列、LLaMA、DeepSeek）采用自回归生成范式，生成第N个token时需以前N-1个token作为完整上下文输入75% similarUnverifiedAutoregressive generation is the text generation paradigm used by all GPT-series models74% similarUnverifiedTraditional large language models (such as GPT-4 in standard inference mode) primarily operate in a System 1-like mode, generating answers directly through a single forward pass.74% similarUnverifiedThe GPT series follows the decoder route, adhering to the autoregressive language model objective, and has revealed the phenomenon of emergent capabilities through scaling.73% similar
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