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Top-p (Nucleus Sampling)

Top-p, also known as nucleus sampling, is a powerful parameter used in language model (LM) requests that controls the diversity of the generated text. Along with the temperature parameter, top-p plays a central role in determining how creative, coherent, or safe the model’s output will be. Unlike temperature, which rescales the probability distribution of all tokens, top-p limits the sampling to the most probable subset of tokens whose cumulative probability mass reaches a certain threshold p. This makes top-p an essential tool for balancing creativity and control.

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What Top-p Is

Top-p (nucleus sampling) works as follows:
  1. The model generates a probability distribution over all possible next tokens.
  2. Tokens are sorted from highest to lowest probability.
  3. The model selects the smallest set of tokens whose cumulative probability is ≥ p.
  4. The next token is sampled only from this set.
For example:
  • Suppose the top token probabilities are [0.4, 0.3, 0.2, 0.05, 0.05].
  • If top_p = 0.8, the model will only consider [0.4, 0.3, 0.2] (cumulative probability = 0.9 ≥ 0.8).
  • Tokens outside this subset (0.05, 0.05) are ignored, reducing the risk of unlikely or irrelevant outputs.
Think of top-p as a dynamic cutoff — it only considers the “nucleus” of high-probability options.

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How Top-p Affects Model Behavior

Top-p influences how deterministic or creative the model output is, in ways that complement the temperature parameter:
Top-p ValueBehaviorExample Use CasesNotes
0.0 – 0.1Extremely conservativeData extraction, structured tasksOnly the very top token(s) are considered. Outputs are highly deterministic, almost identical every time.
0.2 – 0.5Low diversitySummarization, customer supportMostly deterministic but allows slight variation. Prevents repetition and adds mild naturalness.
0.6 – 0.8BalancedGeneral-purpose text, Q&A, light creative writingIncludes enough options for variety without producing irrelevant or incoherent content.
0.9 – 0.95High diversityCreative content, brainstorming, storytellingGenerates varied outputs with some risk of deviation from the prompt.
>0.95Very high diversityExperimental or artistic applicationsAlmost all tokens are considered; output may be highly unpredictable. Rarely used in production for structured tasks.

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Comparison: Top-p vs Temperature

ParameterEffectKey Difference
TemperatureRescales probability distribution globallyControls randomness in selection but considers all tokens
Top-pLimits sampling to the “nucleus” of high-probability tokensProvides a dynamic cutoff, ignoring low-probability tokens entirely
Tip: Top-p and temperature can be combined for fine-grained control. Example: temperature=0.7 and top_p=0.9 → encourages creativity but avoids wild outputs.

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Choosing Top-p for Your Use Case

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Low Top-p (0.0 – 0.5)

  • Use for tasks that require reliability and determinism.
  • Examples: filling forms, extracting structured data, generating code, summarizing official documents.

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Medium Top-p (0.6 – 0.8)

  • Balanced approach for general content creation.
  • Allows natural variation without producing off-topic responses.
  • Examples: blog summaries, FAQs, instructional text, standard Q&A.

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High Top-p (0.9 – 0.95)

  • For creative or generative tasks where variety is desired.
  • Examples: poetry, storytelling, marketing copy, idea generation.

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Extreme Top-p (>0.95)

  • Experimental scenarios only.
  • May produce incoherent or unpredictable outputs.
  • Rarely recommended for production.

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Practical Examples

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Example 1: Same Prompt, Different Top-p

Prompt: “Write a tagline for an AI productivity app.”
  • Top-p = 0.3
    “Work smarter, achieve more.”
    • Short, safe, deterministic.
  • Top-p = 0.7
    “Boost focus, conquer your tasks, and get more done every day.”
    • Balanced, slightly creative.
  • Top-p = 0.95
    “Turn ideas into action, ride the wave of productivity, and make every second count!”
    • Highly creative, risk of slightly over-the-top phrasing.
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