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A language model's policy is designed such that the probability of generating an output is proportional to the exponential of its reward score. If Output Y has a reward score that is exactly double the reward score of Output Z, it means the policy will assign exactly double the probability to Output Y compared to Output Z.
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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
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Plackett-Luce Selection Probability Formula
Optimal Policy as a Product of Reference Policy and Exponentiated Reward
Worth Function in Plackett-Luce Model
A language model's policy, which determines the probability of generating an output
ygiven an inputx, is structured to be proportional to the exponential of a reward scorer(x, y). For a specific input, two potential outputs have the following reward scores:- Output A: Reward = 3.0
- Output B: Reward = 1.0
Based on this formulation, how does the probability of generating Output A compare to the probability of generating Output B?
Analyzing Language Model Response Probabilities
A language model's policy is designed such that the probability of generating an output is proportional to the exponential of its reward score. If Output Y has a reward score that is exactly double the reward score of Output Z, it means the policy will assign exactly double the probability to Output Y compared to Output Z.