Top-k sampling is a text generation technique used in large language models. It involves selecting the top-k most likely next tokens based on their probabilities from the output distribution generated by the language model. The k tokens are then sampled from this set of top-k tokens based on their probabilities.
The top-k sampling technique is useful in generating diverse and creative text, as it allows the language model to explore alternative next-token predictions beyond the single most likely next token. The value of k can be adjusted based on the desired level of creativity or diversity in the generated text.
