Increasing n trades off variance in exchange for less bias. If i understand it correctly, this means that i could calculate the perplexity of a single sentence. I will cite the faq from.
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The only way to know whether increasing n reduces perplexity is by already knowing how exactly how the text.
一、change：perplexity不再做搜索了 1.1 以前的perplexity是什么？ 在讲computer之前，先简单说说perplexity是干什么的。 简单说，它是一个 ai答案引擎。 以前你想知道什么： 打.
显然这与前面打印机的例子相同，因此随机语言模型的 perplexity 为 \vert \mathcal v\vert 。 p.s. What does it mean if i'm asked to calculate the perplexity on a whole corpus? I'm confused about how to calculate the perplexity of a holdout sample when doing latent dirichlet allocation (lda). Walles.ai 、perplexity.ai 可以看到有三个优势： 研究模式搜索了更多的信源并总结，可以看到有多达47个，更具说服力了。 有一段话的回答模式，也会更深入的研究，包含扩展问题，还有.
The papers on the topic breeze over it, making me think i'm missing something ob. Wikipedia article on perplexity does not give an intuitive meaning for the same.
