Top-p sampling, also known as nucleus sampling or softmax sampling with temperature, is a text generation technique used in large language models. It involves selecting the smallest set of next tokens whose cumulative probability exceeds a certain threshold p from the output distribution generated by the language model. The probability mass is distributed among these selected tokens, and a token is sampled from this set based on their probabilities.
The top-p sampling technique is useful in generating diverse and relevant text, as it allows the language model to consider a smaller set of the most likely next tokens, but with a flexible threshold based on the distribution of probabilities. This enables the model to capture the variability in the distribution, and choose from a smaller set of more relevant and meaningful next tokens. The value of p can be adjusted based on the desired level of diversity or relevance in the generated text.
