In information theory, perplexity is a measure of uncertainty for a discrete probability distribution. The perplexity of a fair coin toss is , and that of a fair die roll is ; and generally, for a probability distribution with exactly outcomes each having a probability of exactly , the perplexity is simply . But perplexity can also be applied to unfair dice, and to other non-uniform probability distributions. It can be defined as the exponentiation of the information entropy. The larger the perplexity, the less likely it is that an observer can guess the value which will be drawn from the dist
信息论中,困惑度度量概率分布或概率模型的预测结果与样本的契合程度,困惑度越低则契合越准确。该度量可以用于比较不同模型之优劣。
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Discovered by embedding cosine similarity (sentence-transformers MiniLM, 384-dim).