Top-k

Top-k sampling restricts token sampling to the k most probable tokens at each generation step, setting the probability of all other tokens to zero before re-normalizing and sampling. It prevents the model from selecting very low-probability tokens but is less adaptive than top-p (nucleus) sampling, which adjusts the candidate set size based on probability mass.

Top-k and top-p are often used together.