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How can an understanding of perplexity enhance the performance of language models? In what specific ways does knowledge of this metric contribute to improving model accuracy, coherence, and overall effectiveness in generating text? Additionally, how might it influence training strategies and evaluation processes for natural language processing applications?

In what ways can knowledge of perplexity improve model performance?

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In what ways can knowledge of perplexity improve model performance?

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