A language model predicts the probabilities for the next word in a sequence. The top four candidates are: 'happy' (0.4), 'sad' (0.2), 'angry' (0.1), and 'joyful' (0.05). A decoding method is applied that restricts the possible choices to only the top three candidates ('happy', 'sad', 'angry'). After the probabilities for this smaller set are rescaled to form a new, valid probability distribution, what is the new probability for the word 'sad'?
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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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A language model predicts the probabilities for the next word in a sequence. The top four candidates are: 'happy' (0.4), 'sad' (0.2), 'angry' (0.1), and 'joyful' (0.05). A decoding method is applied that restricts the possible choices to only the top three candidates ('happy', 'sad', 'angry'). After the probabilities for this smaller set are rescaled to form a new, valid probability distribution, what is the new probability for the word 'sad'?
Debugging a Sampling Algorithm
Impact of Vocabulary Set Size on Renormalized Probabilities