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Is maximizing probability same as minimizing perplexity?
Perplexity is a function of the probability of the sentence. The meaning of the inversion in perplexity means that whenever we minimize the perplexity we maximize the probability.
What is the range of perplexity?
The perplexity is 2−0.9 log2 0.9 – 0.1 log2 0.1= 1.38. The inverse of the perplexity (which, in the case of the fair k-sided die, represents the probability of guessing correctly), is 1/1.38 = 0.72, not 0.9. The perplexity is the exponentiation of the entropy, which is a more clearcut quantity.
Why do we use perplexity?
In information theory, perplexity is a measurement of how well a probability distribution or probability model predicts a sample. It may be used to compare probability models. A low perplexity indicates the probability distribution is good at predicting the sample.
Why does lower perplexity indicate more likely data?
I.e, a lower perplexity indicates that the data are more likely. As referenced in your equation, the authors are calculating test set perplexity. In other words, they’re estimating how well their model generalizes by testing it on unseen data.
When do you use perplexity as a metric?
Perplexity is a common metric to use when evaluating language models. For example, scikit-learn’s implementation of Latent Dirichlet Allocation (a topic-modeling algorithm) includes perplexity as a built-in metric.
Is there a benchmark for perplexity in NLP?
It’s hard to provide a benchmark for perplexity because, like most Natural Language tasks, the metric is highly dependent on the vocabulary size. Given a corpus, a smaller vocabulary means that other words will all be replaced with an (out-of-vocabulary) token, instantly increasing the apparent quality of any language model trained on it
How is perplexity related to entropy in NLP?
For WikiText-103, a selection of ~28,000 high-quality Wikipedia articles and a large (0.4% OOV rate) vocabulary, the state-of-the-art perplexity for a language model (as of this writing) is 10.8. Worst-case-scenario. On any dataset, the baseline model is to just guess a word in the vocabulary randomly with equal probability for each.