Perplexity Measures Trend 2026

Perplexity measures how well a probability model predicts a sample, essentially quantifying uncertainty in language models. Lower perplexity indicates better predictive performance. Data scientists and machine learning engineers use it to evaluate, compare, and fine-tune models for tasks like text generation or speech recognition. Natural language processing researchers also rely on it to optimize model architectures.