Perplexity

Perplexity is a measurement used to evaluate language models, indicating how well a probability distribution predicts a sample. It quantifies the uncertainty of a model when predicting the next word in a sequence; lower perplexity values suggest better performance. In essence, perplexity can be thought of as the average branching factor of the model's predictions. Commonly employed in natural language processing tasks, it helps in comparing different models and tuning them for optimal results, making it a crucial metric in the development of effective language models.