Perplexity is a concept used in information theory and natural language processing to measure how well a probability distribution or model predicts a sample. In NLP, perplexity is used to evaluate language models and is defined as the exponentiated average negative log-likelihood of a sequence. A low perplexity indicates that the probability distribution is good at predicting the sample, while a high perplexity indicates the opposite. The size of the vocabulary is relevant to perplexity because as its cardinality is reduced, the number of possible words given any history must also decrease. While perplexity is a common metric for evaluating language models, it has its limitations. For example, it may not be accurate for final evaluation, and it can be hard to make comparisons across datasets. Additionally, perplexity might favor models that are good at predicting frequent words but not rare ones.
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