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Perplexity is a crucial metric in natural language processing (NLP) as it measures how well a probability model predicts a sample. A lower perplexity indicates better predictive performance, reflecting the model's ability to understand and generate human-like text. Understanding perplexity helps researchers evaluate and improve NLP models effectively.

Why is perplexity important in natural language processing?

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Why is perplexity important in natural language processing?

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