Perplexity, in the context of information theory and natural language processing (NLP), is a measurement of how well a probability distribution or probability model predicts a sample. It's often used to evaluate language models, where it measures how uncertain a model is when predicting the next word in a sequence.
Mathematically, perplexity is defined as:
\[
\text{Perplexity}(P) = 2^{H(P)}
\]
where \(H(P)\) is the entropy of the probability