Unverified50% confidenceFactExact time
Transformer 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
1
Sources
50%
Confidence
Long-term
Relevance
7/12/2026
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.85% similarUnverifiedThe hallucination phenomenon in large models is rooted in the autoregressive generation mechanism of Transformers, where the model learns probability distributions through the Next Token Prediction task during the pre-training phase.83% 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80% similarUnverifiedModern large language models are based on the Transformer architecture and generate text by predicting the probability distribution of the next token, with the training objective of minimizing cross-entropy loss76% 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75% similar
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