What is the definition of Top-p sampling?

Top-p sampling, also known as nucleus sampling, is a method used in generating text with AI models that focuses on balancing diversity and coherence. It operates by selecting from a dynamic set of tokens—only those that, when combined, meet or exceed a specified cumulative probability threshold (denoted by p). This means that the model considers a subset of the entire output vocabulary, ensuring that the chosen tokens represent the most relevant options based on their likelihood of completion.

By using this method, Top-p sampling enhances the quality of text generation compared to simply choosing the most probable token, as it allows for the inclusion of less likely options that can contribute to more creative or contextually appropriate outputs. This is particularly valuable in scenarios where diversity in the generated content is desirable, allowing for variations that still fit well within the overall context.

In contrast, selecting only the most probable token or picking the first k tokens disregards the importance of balancing coherence and novelty in generated text. Maximizing diversity without regard for coherence would likely lead to nonsensical outputs, as it does not maintain the necessary connections between words and ideas. Thus, Top-p sampling effectively captures a portion of the vocabulary that is more likely to yield high-quality, contextually relevant responses.