Comparison
Perplexity vs Tokenization
Perplexity and Tokenization are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Perplexity
Perplexity comes up when the question is fundamentally about evaluation.
Perplexity 12 on WikiText is much better than perplexity 30.
When you would reach for Tokenization
Tokenization comes up when the question is fundamentally about inference.
"reading" tokenizes to one token in many tokenizers.
Frequently asked
What is the difference between Perplexity and Tokenization?
Perplexity: Perplexity measures how "surprised" a language model is by held-out text. Lower is better. It is the natural intrinsic eval for next-token prediction. Tokenization: Tokenization is the process of splitting raw text into the discrete tokens an LLM consumes. Most modern LLMs use a learned byte-pair-encoding (BPE) tokenizer.
When should I use Perplexity vs Tokenization?
Perplexity is the right concept when you are focused on evaluation. Tokenization applies when you are focused on inference.
Are Perplexity and Tokenization the same thing?
No. Perplexity is evaluation; Tokenization is inference. They are related but address different parts of the AI stack.