Applying Pruning in Sequence Generation
In a sequence generation task, the set of candidate sequences at step i-1 contains 3 distinct sequences (i.e., |Y_{i-1}| = 3). The vocabulary size is 1,000 (i.e., |V| = 1,000). The generation process uses the formula Y_i = Prune(Y_{i-1} × V), where the Prune(·) function is configured to keep only the 2 most promising sequences.
Based on this information, calculate:
- The size of the full set of expanded hypotheses (
Y_{i-1} × V) before pruning. - The size of the final set of candidate sequences (
Y_i) after pruning.
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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
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Related
In a sequence generation process, the set of candidate sequences at step
i, denotedY_i, is generated from the previous setY_{i-1}and the entire vocabularyV. Consider the difference between two methods for generatingY_i:Method A:
Y_i = Y_{i-1} × VMethod B:Y_i = Prune(Y_{i-1} × V)What is the most significant practical difference in the outcome of using Method B instead of Method A, particularly for generating longer sequences?
Diagnosing a Failing Sequence Generation Algorithm
Applying Pruning in Sequence Generation