Perplexity is a measure used in various fields, including information theory and natural language processing, to quantify uncertainty or complexity. In the context of language models, it indicates how well a model predicts a sample. A lower perplexity score suggests that the model is more confident and accurate in its predictions, while a higher score indicates greater uncertainty. In practical terms, perplexity can be thought of as the average number of choices a model has when predicting the next word in a sentence. For example, if a model has a perplexity of 10, it means it is as uncertain as if it had to choose from 10 equally likely options for the next word.