Top-p (Nucleus Sampling)
LLM & Language ModelsA generation parameter that controls which tokens the model considers — only tokens within the top probability mass are eligible, filtering out unlikely choices.
Top-p (also called nucleus sampling) is a complementary parameter to temperature that controls text generation randomness. While temperature scales all probabilities, top-p sets a cumulative probability threshold — only tokens whose combined probabilities reach the top-p value are considered.
With top-p = 0.9, the model only considers tokens that together make up 90% of the probability mass, ignoring the long tail of unlikely tokens. With top-p = 0.1, only the very top choices are considered, making output more deterministic.
In practice you adjust either temperature or top-p, not both at once — together they interact in ways that are hard to reason about. OpenAI's API reference says as much, recommending you alter one or the other but not both. Many AI tools never expose top-p to users at all and set it internally.
Real-World Example
If an AI's output is too random or nonsensical, lowering top-p to 0.8 or 0.9 constrains it to more probable word choices without making it completely rigid.
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What is Top-p (Nucleus Sampling)?
A generation parameter that controls which tokens the model considers — only tokens within the top probability mass are eligible, filtering out unlikely choices.
How is Top-p (Nucleus Sampling) used in practice?
If an AI's output is too random or nonsensical, lowering top-p to 0.8 or 0.9 constrains it to more probable word choices without making it completely rigid.
What concepts are related to Top-p (Nucleus Sampling)?
Key related concepts include Temperature, Token, LLM (Large Language Model), Inference. Understanding these together gives a more complete picture of how Top-p (Nucleus Sampling) fits into the AI landscape.