What does perplexity measure in language models?

Perplexity is a measurement used in evaluating language models that reflects how well a probability distribution or probability model predicts a sample. Specifically, in the context of language models, perplexity measures the likelihood of a model generating a sequence of words. It quantifies the uncertainty or complexity of the model's predictions, with lower perplexity indicating that the model is more confident in its predictions.

In language modeling, a model generates a word sequence by predicting the next word based on the previous words. Perplexity evaluates how many choices the model considers at each step; fewer choices correspond to lower perplexity, indicating that the model is effectively capturing the patterns of the language.

This concept revolves around the idea that a model that can predict word sequences with high accuracy will have a lower perplexity value, demonstrating that it has learned the underlying structure and usage of the language well. Therefore, the correct choice accurately conveys the role of perplexity in assessing language models by focusing on the probability related to word sequence generation.