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Rationale for the Negative Expected Utility Loss Function
The primary goal when refining a language model's policy is to maximize the expected utility of its generated outputs. However, the loss function for this optimization process is typically defined as the negative of the expected utility. Explain the fundamental reason for this specific formulation. Why is minimizing the negative expected utility equivalent to maximizing the expected utility, and why is this approach commonly used in training machine learning models?
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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
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An engineer is training a language model where the training objective is to adjust the model's parameters to maximize a utility score for its generated outputs. The loss function is defined as the negative of the expected utility score. During a training run, the engineer observes that the calculated loss value is consistently increasing over several iterations (e.g., moving from -15.0 to -12.5 to -10.0). What is the most direct interpretation of this observation?
Rationale for the Negative Expected Utility Loss Function
Consequences of an Incorrect Loss Function Implementation