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AI / LLM Basics Interview Questions

What is Perplexity in language modeling?

Perplexity is a metric that measures how well a language model predicts a given piece of text, essentially quantifying how "surprised" the model is by the actual next words.

  • Lower perplexity means the model assigned higher probability to what actually happened next, indicating a better fit to that text
  • Commonly used to evaluate and compare language models during development, especially on held-out test data
  • Doesn't directly measure whether a model's output is factually correct or genuinely useful, just how predictable the text was to the model

Perplexity is a useful, easy-to-compute proxy for language modeling quality, but it's just one signal among several used to judge whether a model is actually good at real tasks.

What does lower perplexity indicate?
Does perplexity directly measure factual correctness?

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