Evaluating Preference Model Performance with Listwise Loss
A preference model is being trained using a loss function that is calculated by summing the losses of all individual pairwise comparisons derived from a ranked list. Based on the human preference and the two model predictions below, which model would incur a higher training loss for this specific example? Justify your reasoning by explaining how the number of incorrect pairwise comparisons contributes to the total loss.
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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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Listwise Loss Formula from Accumulated Pairwise Comparisons
A human annotator is given four model-generated responses (A, B, C, D) to a prompt and ranks them in order of preference from best to worst as: C > A > D > B. To train a preference model, a loss function is calculated by summing the individual losses for every pairwise comparison implied by this ranking. Which of the following sets represents all the pairwise preferences that would be used in this loss calculation?
Decomposing a Ranked List into Pairwise Preferences
Evaluating Preference Model Performance with Listwise Loss