Perplexity is a measurement used to evaluate the degree of uncertainty or confusion of a language model when predicting a given sample. It quantifies how well the language model can predict the next word or sequence of words in a text. Lower perplexity indicates better predictive performance.

Perplexity is calculated by taking the inverse probability of the words in the sample, normalized by the number of words. It essentially measures how surprised the language model would be when encountering the given sample. A lower perplexity value signifies that the model is more certain about its predictions and has a better understanding of the language.

Perplexity is derived from the concept of entropy in information theory. It can be thought of as the average number of bits required to represent each word in a sample. A lower perplexity value indicates a more efficient and accurate language model, as it can predict the next word with lesser uncertainty.