Unverified50% confidenceFactExact time
When the probability distribution during the decoding phase fluctuates (such as when the temperature parameter is too high or the context matches the training distribution of a particular language), the model may slip into the semantic space of another language when predicting the next token.
1
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50%
Confidence
Long-term
Relevance
9/19/2026
First Seen
Sources
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Related Claims
VerifiedThe essence of large language models is learning the conditional probability distribution of what the next word is given the preceding text79% similarVerifiedWhen a large language model generates each token, it samples from a probability distribution; the temperature parameter controls the degree of randomness—higher temperature produces more diverse outputs, while lower temperature produces more deterministic ones.78% similarUnverifiedThe output of large language models is inherently probabilistic, and parameters such as sampling temperature and Top-P mean the model may generate different content each time, posing a challenge for software testing.76% similarUnverified大语言模型通过自监督预训练学习token序列的条件概率分布，本质上是统计压缩模型，无法通过统计特征区分正确与错误的数学证明76% similarUnverified大语言模型是概率性、随机性的，它根据之前出现的词元来预测下一个词元75% similar
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