From Model Scores to Probabilities
A language model has evaluated three possible next words in a sequence and assigned them the following unnormalized scores: 'excellent' (score: 4.5), 'good' (score: 3.0), and 'okay' (score: 1.5). Explain the process required to convert these scores into a valid probability distribution and state why this conversion is necessary for the model to make a probabilistic choice.
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Ch.4 Alignment - 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
Science
Related
Normalization Factor for a Reward-Weighted Policy
A function assigns the following unnormalized scores to three possible discrete outcomes:
score(A) = 12,score(B) = 7, andscore(C) = 1. To transform these scores into a valid probability distributionP(outcome), each score must be divided by a normalization factor calculated from the sum of all scores. What is the resulting probability for outcome B,P(B)?From Model Scores to Probabilities
Converting Model Scores to Probabilities