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In a listwise ranking task, if the training objective is to minimize the negative log-likelihood of the ground-truth ranked sequences, a decrease in the loss value over training epochs signifies that the model is assigning a lower probability to the correct sequences.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
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Comprehension in Revised Bloom's Taxonomy
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Plackett-Luce Loss Formula
A model is being trained for a listwise ranking task. For one training example, it must rank three items: Item X, Item Y, and Item Z. The correct, ground-truth ranking is X > Y > Z. The training objective is to minimize the negative log-likelihood of observing this ground-truth sequence. Which expression correctly represents the quantity to be minimized for this single training instance, where P(A | S) is the probability of choosing item A from the set of available items S?
Analyzing Model Error with Plackett-Luce Loss
In a listwise ranking task, if the training objective is to minimize the negative log-likelihood of the ground-truth ranked sequences, a decrease in the loss value over training epochs signifies that the model is assigning a lower probability to the correct sequences.