Perplexity

Perplexity is a measurement used to evaluate how well a language model predicts a set of words or text. Think of it as gauging the model's surprise: the lower the perplexity, the better the model is at predicting what comes next, indicating it understands the language better. For example, if a model has low perplexity on a sentence, it means it's confident and accurate in its predictions. High perplexity suggests the model finds the text unpredictable or confusing. In essence, it quantifies the model’s uncertainty, helping assess its effectiveness in understanding and generating language.