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?
More Related questions...
