Top-P, also known as nucleus sampling, is a configuration setting used in Large Language Models (LLMs) to restrict the predicted next token to come from tokens with the top predicted probabilities[1]. This sampling setting helps to control the randomness and diversity of generated text[1]. Top-P sampling selects the top tokens whose cumulative probability does not exceed a certain value (P)[1]. Values for P range from 0 (greedy decoding) to 1 (all tokens in the LLM’s vocabulary)[1].
The best way to choose between top-K and top-P is to experiment with both methods (or both together) and see which one produces the results you are looking for[1].
Get more accurate answers with Super Search, upload files, personalized discovery feed, save searches and contribute to the PandiPedia.
Let's look at alternatives:
- Modify the query.
- Start a new thread.
- Remove sources (if manually added).
- Request a manual search from our human research team.