Perplexity

Summary

Perplexity is a measure of how well a probability model predicts a sample.

Abstract

Perplexity is a key concept in natural language processing and machine learning, used to evaluate language models and other generative models. It measures how well a model can predict a sample, with lower perplexity indicating better performance. Perplexity is calculated as the exponential of the cross-entropy between the model's predicted probabilities and the actual outcomes. It is often used to compare different models or to evaluate the performance of a single model over time.

Bullet Points

• •Perplexity is a measure of how well a probability model predicts a sample.
• •Perplexity is calculated as the exponential of the cross-entropy between the model's predicted probabilities and the actual outcomes.
• •Perplexity is often used to compare different models or to evaluate the performance of a single model over time.