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Related Questions
- What are the scenarios where perplexity is a better choice than accuracy for evaluating language models?
- When does perplexity provide a more nuanced understanding of a model's performance compared to accuracy?
- Can you give examples of how perplexity is used in real-world applications to assess language model quality?
- How does perplexity differ from accuracy in terms of the types of errors it can detect?
- Can you explain why perplexity is a more suitable metric for evaluating language models in certain domains, such as language translation?
- What are the limitations of using perplexity as a metric for evaluating language models, and when might accuracy be a better choice?
- Can you provide examples of how perplexity is used in natural language processing tasks, such as text classification or sentiment analysis?
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