Top-p vs. top-k sampling
Imbalanced dataset
Linear regression with gradient descent
Imbalanced dataset
Linear regression with gradient descent
Machine Learning
Medium
Seen in real interview
What is top-k sampling and how does it differ from top-p sampling?
Both are sampling techniques to generate text in LLMs. Top-k sampling reduces computation workload by estimating the softmax probabilities based on the logits of the top-k tokens.
On the other hand, top-p samples tokens until the cummulative probability reaches a value p.
Below is a table that summarizes the differences between the two methods:
| Feature | Top-k Sampling | Top-p (Nucleus) Sampling |
|---|---|---|
| Definition | Samples from the top k most likely tokens | Samples from the smallest set of tokens with cumulative prob ≥ p |
| Flexibility | Fixed token count (k) |
Dynamic token count (based on probability mass p) |
| Accuracy / Fluency | Good, but can miss rare contextually appropriate tokens | Higher fluency; captures context-sensitive rare tokens better |
| Computation Cost | Lower (fixed-size sampling from k logits) | Slightly higher (must compute all token probabilities) |
| Speed | Slightly faster due to fixed cutoff | Slightly slower due to dynamic cutoff |
| Typical Use Case | Faster generation with controlled randomness | More natural, human-like generation |
LLMs, Sampling
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