Empirical Formulation of Pair-wise Ranking Loss
The pair-wise ranking loss function for a reward model with parameters can be formulated by summing over samples from the preference dataset . The expected loss incorporates the probability of drawing an input, and the conditional probability of drawing the preferred output pair. Assuming a uniform distribution over model inputs involved in sampling, the formula simplifies to:
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.4 Alignment - Foundations of Large Language Models
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Empirical Formulation of Pair-wise Ranking Loss
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A reward model is being trained to prefer one machine-generated text response over another for a given input. The training process aims to minimize a loss function calculated as the negative logarithm of a sigmoid applied to the difference between the reward scores of the preferred () and non-preferred () responses. Given the following reward scores assigned by the model to a single pair of responses, which scenario contributes the least to the total loss, indicating the model is correctly differentiating between the responses?
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Empirical Formulation of Pair-wise Ranking Loss
A system learns a function,
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Empirical Formulation of Pair-wise Ranking Loss
Learn After
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