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A language model is generating the next token in two different contexts. In both contexts, the model uses a sampling method where it forms a candidate pool by selecting the smallest set of the most probable tokens whose cumulative probability exceeds a threshold of 0.9.
- Context A: The single most probable token has a probability of 0.95.
- Context B: The ten most probable tokens each have a probability of 0.09.
How will the size of the candidate token pool compare between these two contexts?
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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
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
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Mathematical Representation of the Top-p Candidate Pool
A language model is generating the next word and has calculated the following probabilities for the most likely tokens: Token A (0.40), Token B (0.30), Token C (0.15), Token D (0.10), and Token E (0.05). If the model uses a sampling strategy where it forms a candidate pool by including the most probable tokens until their cumulative probability just exceeds a threshold of 0.75, what will be the size of this candidate pool?
Relationship Between Threshold and Candidate Pool Size
A language model is generating the next token in two different contexts. In both contexts, the model uses a sampling method where it forms a candidate pool by selecting the smallest set of the most probable tokens whose cumulative probability exceeds a threshold of 0.9.
- Context A: The single most probable token has a probability of 0.95.
- Context B: The ten most probable tokens each have a probability of 0.09.
How will the size of the candidate token pool compare between these two contexts?