Token Probability

Token probability refers to the likelihood that a language model assigns to a specific word (token) appearing at a given position in a text sequence. AI detectors analyze token probabilities to determine whether text was generated by an AI model.

How Token Probability Relates to AI Detection

When an AI model generates text, it selects tokens with high probability — words that statistically fit the context. This creates a pattern:

• AI-generated text contains mostly high-probability tokens (the model selected them precisely because they were likely)
• Human-written text contains a natural mix of high and low-probability tokens (humans make unexpected word choices, use rare vocabulary, and break patterns)

Token Probability and Perplexity

Token probability is the foundation of perplexity scoring. Perplexity is calculated from the average token probabilities across a text:

• High average token probability → Low perplexity → More likely AI
• Mixed token probabilities → Higher perplexity → More likely human

How Detectors Use Token Probabilities

AI detectors run the submitted text through a reference language model and calculate probabilities for each token. They then analyze:

1. The average probability across all tokens
2. The distribution of probabilities (variance)
3. Whether the probability pattern matches known AI generation signatures
4. Runs of consistently high-probability tokens

FAQ

Q: Can you see the token probabilities for your AI-generated text?

A: Some AI APIs (like OpenAI's) provide logprobs (log probabilities) for generated tokens. These show exactly how "surprised" the model was at each word choice.

Q: How do humanizers affect token probabilities?

A: AI humanizers introduce tokens that a model would assign lower probability to — making the overall distribution look more like human writing without changing the text's meaning.