AI Glossary Term
What does “Perplexity” mean?
Definition
A language-model metric measuring how surprised the model is by true next tokens.
How Perplexity works
Example
A language model reads a held-out news article and rates how likely each actual next word was. If it kept assigning low probability to the real words, perplexity is high, which signals a weaker fit to that kind of text.
Why it matters
Perplexity is a standard way to compare language models on the same text. It does not by itself measure helpfulness or truthfulness.
Common questions
Is lower perplexity better?
Yes, for comparison on the same data and tokenization. It means the model assigned higher probability to the actual text.
Can I compare perplexity across models?
Only fairly if they use the same tokenizer and test text.
Does low perplexity mean accurate answers?
No. It measures fit to text, not whether statements are correct or useful.