A language model is being trained with a supervised objective to maximize the probability of the correct output. Given the input 'The largest city in the US is', the target output is the two-token sequence 'New York'. Two different models are evaluated on this single instance.
- Model A predicts the first token 'New' with a probability of 0.6, and then predicts the second token 'York' with a probability of 0.8.
- Model B predicts the first token 'New' with a probability of 0.9, and then predicts the second token 'York' with a probability of 0.4.
Based on the standard training objective for this task, which statement correctly analyzes the models' performance on this specific example?
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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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Analysis in Bloom's Taxonomy
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A language model is being trained with a supervised objective to maximize the probability of the correct output. Given the input 'The largest city in the US is', the target output is the two-token sequence 'New York'. Two different models are evaluated on this single instance.
- Model A predicts the first token 'New' with a probability of 0.6, and then predicts the second token 'York' with a probability of 0.8.
- Model B predicts the first token 'New' with a probability of 0.9, and then predicts the second token 'York' with a probability of 0.4.
Based on the standard training objective for this task, which statement correctly analyzes the models' performance on this specific example?
Analyzing Model Training with Flawed Data
Limitations of Supervised Fine-Tuning for LLM Alignment
Parameter Updates in Supervised LLM Training