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A language model processes an input prompt x and considers four potential output sequences: y1, y2, y3, and y4. The model calculates the conditional probability Pr(y|x) for each sequence as follows:
Pr(y1|x) = 0.45Pr(y2|x) = 0.15Pr(y3|x) = 0.85Pr(y4|x) = 0.60
If the model employs a selection method that chooses the top 2 sequences that maximize this probability, which set of sequences will be the final output?
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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
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A language model processes an input prompt
xand considers four potential output sequences:y1,y2,y3, andy4. The model calculates the conditional probabilityPr(y|x)for each sequence as follows:Pr(y1|x) = 0.45Pr(y2|x) = 0.15Pr(y3|x) = 0.85Pr(y4|x) = 0.60
If the model employs a selection method that chooses the top 2 sequences that maximize this probability, which set of sequences will be the final output?
Interpreting the BoN Sampling Formula
In the mathematical formulation of a sampling method where the top N outputs are selected based on their likelihood given an input
x, the expressionargTopN_y [Pr(y|x)]returns the N highest conditional probability values.