Perplexity is a tool used in natural language processing to measure the effectiveness of language models in predicting a given sequence of words. It is calculated as the inverse probability of a test set normalized by the number of words. Lower perplexity scores indicate better performance. Perplexity is commonly used in tasks such as speech recognition, machine translation, and text generation. It provides a way to compare the performance of different language models and can help in optimizing their hyperparameters for better results.
