Verified80% confidenceFactExact time
Large language models are trained on vast corpora of text data and learn to predict the probability distribution of the next word
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Long-term
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8/2/2026
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Verified大语言模型通过在数千亿乃至数万亿个词元（Token）的文本数据上进行自监督预训练81% similarVerifiedWhen generating text, language models default to selecting word sequences with higher conditional probability.76% similarUnverifiedLarge language models generate text by maximizing the statistical probability of the next word, so they tend to choose the most frequent, safest, and least offensive wording appearing in the training corpus, which is the source of ritual greetings, excessive politeness, and filler.74% similarUnverifiedTraditional autoregressive language models sample from the entire vocabulary (typically tens of thousands to hundreds of thousands of tokens) or select the highest-probability token during generation.73% similar
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