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Also known as: Top-p Sampling
Sampling from the smallest set of tokens whose cumulative probability exceeds p.
DESCRIPTION
Nucleus sampling selects from the minimal set of tokens whose cumulative probability mass exceeds threshold p. Unlike Top-k, the candidate set adapts to distribution shape, improving fluency in heavy-tailed contexts. Proper p balances creativity and coherence; values around 0.8–0.95 are common. It often pairs with temperature to refine randomness. Introduced to combat neural text degeneration from low-probability tails.
ALTERNATIVE DEFINITIONS
arXiv
We propose nucleus sampling, which considers the smallest set of tokens whose cumulative probability mass exceeds the threshold p.
Original paper defining the core idea and motivation.
Hugging Face
Nucleus (top-p) sampling chooses from the smallest set of tokens with cumulative probability ≥ p.
Matches paper definition and clarifies cumulative mass criterion.
RELATED TERMS
Temperature Greedy Decoding Beam Search Language Model Probability Mass Entropy Decoding Strategies
COMPETING TERMS
RELATIONS
- Broader: Sampling
KNOWLEDGE AREAS
TARGET AUDIENCE
AI Engineer Data Scientist MLOps Engineer
EXTERNAL SOURCES
- Wikipedia - Language model
- arXiv - Original nucleus sampling paper
- Hugging Face - Top-p sampling documentation
- OpenAI - Discussion of decoding quality
TOP QUESTIONS
How does Nucleus Sampling work?
It dynamically selects the smallest token set whose cumulative probability exceeds p, then samples from that set.
Category: Definition
Why use Nucleus instead of Top-k?
It adapts to distribution shape, avoiding degenerate tails when the top probabilities are diffuse or peaked.
Category: Comparison
How should I pick p?
Start around 0.9 and adjust: lower p for coherence, higher p for diversity, validating against your task metrics.
Category: How-to
Can I combine p with temperature?
Yes. Temperature modulates randomness within the nucleus; tune jointly for desired style and accuracy.
Category: Application
Does Top-p ensure factuality?
No. It shapes diversity, not truthfulness. Use retrieval, constraints, or post-hoc verification for factuality.
Category: Misunderstanding
What risks does Nucleus Sampling have?
Too high p can introduce rambling or off-topic content. Calibrate carefully per domain.
Category: Risk
Is Nucleus deterministic?
No. It is stochastic; outputs vary across runs even with the same p.
Category: Definition
How to evaluate Top-p outputs?
Use human and automated metrics. Compare across p values, with baselines like greedy or Top-k.
Category: Evaluation
Where did Nucleus Sampling originate?
It was proposed by Holtzman et al. in 2019 to mitigate neural text degeneration.
Category: Origin