Unverified50% confidenceFactExact time
LLM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
1
Sources
50%
Confidence
Long-term
Relevance
7/17/2026
First Seen
Sources
Related Claims
UnverifiedThe core objective of pre-training is to give the model the ability to predict the next token (Predict Next Token), based on the causal attention mechanism in the Transformer architecture.82% similarUnverifiedMainstream LLMs are based on the Transformer architecture and acquire capabilities through self-supervised learning of next-token prediction on massive text data.81% similarUnverifiedThe core capability of modern LLMs lies in 'next token prediction,' generating coherent text through statistical language patterns rather than truly understanding semantics.80% similarUnverifiedTransformer 在预训练阶段通过 Next Token Prediction 任务学习概率分布，每次输出本质是在给定上文条件下预测下一个词最可能是什么80% similarVerifiedThe training objective of a Base LLM is to predict the next word, using a paradigm called Autoregressive Language Modeling78% similar
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