Top-P (Nucleus Sampling)
Top-P sampling—also called nucleus sampling—is a decoding strategy used in language models to generate more coherent and diverse text. It selects the next token from the smallest possible set of candidates whose cumulative probability exceeds a threshold p.
Purpose
Top-P limits randomness while maintaining flexibility. Unlike Top-K (which selects from the K most probable tokens), Top-P chooses dynamically based on probability mass, which adapts better to varying uncertainty in predictions.
How It Works
- Sort all possible next tokens by probability (highest to lowest).
- Accumulate probabilities until the sum exceeds the threshold p (e.g., 0.9).
- Sample the next token from this truncated set.
Parameters
- p (probability threshold): A float between 0 and 1.
- Lower p (e.g., 0.7): More focused, deterministic output.
- Higher p (e.g., 0.95): More diverse, creative output.
Comparison: Top-K vs Top-P
| Strategy | Description | Behavior |
|---|---|---|
| Top-K | Pick from fixed top K tokens | Consistent but may miss nuance |
| Top-P | Pick from dynamic set with cumulative P | More adaptive and fluent output |
Use Cases
- Creative writing
- Conversational agents
- Any LLM generation task where a balance of randomness and relevance is needed
Tips
- Often combined with Temperature to fine-tune creativity vs precision.
- Set Top-P ~0.8–0.95 for human-like fluency without excessive randomness.
- Low values (0.1-0.5) produce focused outputs, medium (0.6-0.9) balance creativity and coherence, high (0.9-0.99) enable creative diversity.
Related Notes
- Temperature (Sampling Parameter)
- Top-K Sampling
- Sampling Parameters
- Prompt Engineering Roadmap
- LLM Generation
Top-K Sampling
Top-K sampling is a decoding method used in language models to control output randomness and maintain relevance during token generation. It
Temperature (Sampling Parameter)
Temperature is a key sampling parameter that controls the level of randomness in language model outputs. It adjusts the probability distribution
Sampling Parameters
Sampling parameters control how language models choose the next token during generation. They directly affect the randomness, coherence, and
Decoding Strategies
Decoding strategies define how language models convert predicted token probabilities into coherent text. They control determinism, diversity, and
Greedy Decoding
Greedy decoding is the simplest decoding strategy for language models. At each generation step, it selects the token with the highest probability,