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- intuition - What is perplexity? - Cross Validated
So perplexity represents the number of sides of a fair die that when rolled, produces a sequence with the same entropy as your given probability distribution Number of States OK, so now that we have an intuitive definition of perplexity, let's take a quick look at how it is affected by the number of states in a model
- 如何评价perplexity ai，会是未来搜索的趋势吗？ - 知乎
Perplexity AI 不是搜索的终点，但可能是我们逃离“信息垃圾场”的起点。 它就像是搜索引擎界的 GPT-4：懂你说什么，还知道去哪儿找答案。
- 求通俗解释NLP里的perplexity是什么？ - 知乎
所以在给定输入的前面若干词汇即给定历史信息后，当然语言模型等可能性输出的结果个数越少越好，越少表示模型就越知道对给定的历史信息 \ {e_1\cdots e_ {i-1}\} ，应该给出什么样的输出 e_i ，即 perplexity 越小，表示语言模型越好。
- Comparing Perplexities With Different Data Set Sizes
Would comparing perplexities be invalidated by the different data set sizes? No I copy below some text on perplexity I wrote with some students for a natural language processing course (assume log log is base 2): In order to assess the quality of a language model, one needs to define evaluation metrics One evaluation metric is the log-likelihood of a text, which is computed as follows
- 如何评价 Perplexity 消除了 DeepSeek 的审查以提供 . . . - 知乎
如何评价 Perplexity 消除了 DeepSeek 的审查以提供公正、准确的回答？ Perplexity: 我们很高兴地宣布，全新 DeepSeek R1 模型现已在所有 Perplexity 平台上线。
- clustering - Why does larger perplexity tend to produce clearer . . .
Why does larger perplexity tend to produce clearer clusters in t-SNE? By reading the original paper, I learned that the perplexity in t-SNE is 2 2 to the power of Shannon entropy of the conditional distribution induced by a data point
- machine learning - Why does lower perplexity indicate better . . .
The perplexity, used by convention in language modeling, is monotonically decreasing in the likelihood of the test data, and is algebraicly equivalent to the inverse of the geometric mean per-word likelihood A lower perplexity score indicates better generalization performance I e, a lower perplexity indicates that the data are more likely
- Why do I get weird results when using high perpexity in t-SNE?
I played around with the t-SNE implementation in scikit-learn and found that increasing perplexity seemed to always result in a torus circle I couldn't find any mentions about this in literature
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