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A text generation system is designed to explore multiple possible sentence continuations at each step. It does this by selecting a fixed number of the most probable next words from its entire vocabulary. Match each parameter setting or concept with its most likely consequence or definition.
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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
Social Science
Empirical Science
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Construction of Top-K Candidate Sequences in Beam Search
Mathematical Definition of Top-K Token Selection
A language model is generating text using a search algorithm. At a certain step, it has the partial sequence 'The cat sat on the' and calculates the following probabilities for the next word from its vocabulary:
Word Probability mat 0.45 rug 0.25 chair 0.15 floor 0.10 table 0.03 window 0.02 If the algorithm is configured to select the 3 most probable next words at this step, which set of words will be chosen to create new candidate sequences?
Debugging a Text Generation System
A text generation system is designed to explore multiple possible sentence continuations at each step. It does this by selecting a fixed number of the most probable next words from its entire vocabulary. Match each parameter setting or concept with its most likely consequence or definition.