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Applying the Top-p Sampling Process
A language model has produced the following probability distribution for the next token: {'sky': 0.50, 'ocean': 0.25, 'sea': 0.15, 'water': 0.08, 'blue': 0.02}. If the model is using a sampling strategy with a probability threshold of p = 0.80, identify the tokens that form the 'nucleus' and calculate their new, renormalized probabilities.
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Application in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
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Candidate Pool Size in Top-p Sampling (kp)
Forming the Candidate Pool in Top-p Sampling
A language model is generating text and has calculated the following probabilities for the next possible token: 'the' (0.45), 'a' (0.25), 'one' (0.15), 'it' (0.10), 'she' (0.05). If the model uses a sampling strategy with a probability threshold of
p = 0.8, which set of tokens will form the final candidate pool (the 'nucleus') from which the next token is actually sampled?A language model is configured to generate text by sampling from the smallest set of tokens whose cumulative probability exceeds a predefined threshold 'p'. Arrange the following steps of this process in the correct chronological order.
Applying the Top-p Sampling Process