Amostragem por Núcleo, also known as amostragem top-p, is a technique used in processamento de linguagem natural (NLP) for generating text based on modelos probabilísticos. It is particularly popular in the context of large language models like GPT-3.
Em métodos tradicionais de amostragem, como amostragem top-k, the model selects from the top ‘k’ most probable next words based on the output probabilities. Nucleus Sampling, however, takes a different approach by focusing on a dynamic subset of words. It defines a threshold ‘p’ (where 0 < p ≤ 1) and selects the smallest set of words whose cumulative probability exceeds 'p'. This means that instead of a fixed number of words, the selection can vary in size depending on the model's output distribution.
A principal vantagem da Amostragem de Núcleo é sua capacidade de equilibrar creativity and coherence in generated text. By allowing the model to consider a varying number of options, it can produce more diverse and contextually appropriate responses. For example, if a word has a high probability but is not in the top ‘k’, it can still be chosen if it falls within the nucleus defined by ‘p’.
Este método é especialmente útil em aplicações como chatbots, story generation, and other NLP tasks where a more human-like generation of language is desired. By controlling the threshold ‘p’, users can influence the randomness and variability of the output, leading to richer and more engaging text.