A language model's performance on a single training sample is measured by calculating the negative logarithm of the probability it assigns to the correct target output sub-sequence, given an input sequence. Consider two models, Model A and Model B, being evaluated on the same sample. For this sample, Model A assigns a probability of 0.8 to the correct target sub-sequence, while Model B assigns a probability of 0.2. Based on this information, which statement correctly analyzes the models' performance on this specific sample?
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
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Ch.3 Prompting - Foundations of Large Language Models
Analysis in Bloom's Taxonomy
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Selective Gradient Propagation for Sub-sequence Loss
A language model's performance on a single training sample is measured by calculating the negative logarithm of the probability it assigns to the correct target output sub-sequence, given an input sequence. Consider two models, Model A and Model B, being evaluated on the same sample. For this sample, Model A assigns a probability of 0.8 to the correct target sub-sequence, while Model B assigns a probability of 0.2. Based on this information, which statement correctly analyzes the models' performance on this specific sample?
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