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Impact of Model Confidence on Training Loss
A model is being trained to identify named entities in text. The training process uses a loss function calculated as the average negative logarithm of the probability assigned to the correct entity tag for each word. Consider two individual words from a training sentence. For Word A, the model assigns a probability of 0.99 to the correct tag. For Word B, the model assigns a probability of 0.01 to the correct tag. Analyze and compare the contribution of each of these words to the total training loss. Which word will have a significantly larger impact on the loss, and why is this behavior desirable for training the model?
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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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Analysis in Bloom's Taxonomy
Cognitive Psychology
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Calculating Model Training Loss
A model is being trained for a text labeling task where the goal is to maximize the probability assigned to the correct label for each word. The training loss is calculated as the average of the negative logarithm of these probabilities. Consider the model's performance on one sentence, evaluated by two different sets of parameters (Model A and Model B). The table below shows the probability each model assigned to the correct label for each of the seven words in the sentence.
Word Model A Probability Model B Probability Word 1 0.9 0.8 Word 2 0.8 0.6 Word 3 0.7 0.6 Word 4 0.9 0.8 Word 5 0.9 0.8 Word 6 0.1 0.7 Word 7 0.9 0.8 Based on this data, which model would have a lower training loss for this specific sentence, and why?
Impact of Model Confidence on Training Loss