How decoding strategies limit tokens and balance quality vs. diversity
Top-K and Top-P are two approaches to constrain token selection. Top-K takes the K most probable tokens, Top-P (Nucleus Sampling) takes all tokens up to cumulative probability P. Both have different strengths.
Deep dive into Sampling (3/4) – technical comparison of decoding strategies.
Top-P has largely replaced Top-K because it's adaptive: Narrow for clear decisions, wide for uncertainty. Yet Top-K remains relevant for certain applications.
The simplest decoding method is Greedy Decoding: Choose the token with the highest probability. This is deterministic and leads to consistent but often boring outputs.
Stochastic Sampling allows variability: Sample tokens according to their probability distribution. This increases creativity but can lead to "illogical nonsense."
To find this balance, modern LLMs use filtering strategies that limit the number of candidate tokens:
Top-k Sampling keeps the k tokens with the highest probabilities. All other tokens are set to probability 0, and the distribution is renormalized.
Advantage: Simple, predictable. With k=5, only 5 tokens are ever considered.
Disadvantage: Rigid. For very confident predictions (e.g., "The capital of France is ___"), k=5 unnecessarily limits candidates. For uncertain tasks, k=5 may be too restrictive.
Top-p Sampling (also called Nucleus Sampling) keeps the smallest set of tokens whose cumulative probability ≥ p. This is adaptive.
Advantage: Adaptive. For confident predictions (high probability for top token), few tokens are needed. For uncertain ones, p=0.9 might include 20+ tokens.
Disadvantage: More complex to understand and implement. A threshold p=0.9 has different meaning depending on context type.
| Use Case | Top-k Setting | Top-p Setting | Temperature | Rationale |
|---|---|---|---|---|
| Factual (Search, QA) | k=1 (Greedy) | p=1.0 (No Filter) | T=0.0-0.3 | One correct answer preferred |
| General (Chat) | k=40-50 | p=0.9-0.95 | T=0.7-0.9 | Balances coherence and variability |
| Creative (Storytelling) | k=100+ | p=0.95+ | T=1.0-1.5 | More creativity, less coherence |
| Code Generation | k=5-10 | p=0.8-0.9 | T=0.0-0.5 | Syntactic correctness is important |
In practice: Top-p is used more often than Top-k because it's more adaptive. OpenAI uses p=1.0 by default, but many users set p=0.9 for better quality. The combination with Temperature T is important for finer control.
Top-k and Top-p balance between deterministic correct answers and creative, varied outputs.
Top-p adapts to uncertainty: Automatically more restrictive for confident predictions.
Temperature + Top-k/p work together. Both need to be tuned for optimal results.
Top-p=0.9 + Temperature=0.7 is a good default for most tasks.
Too restrictive = boring. Too permissive = incoherent. The balance depends on the task.
Good sampling is as important as model size for generation quality. Often underestimated.