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Log-Probability Loss with Model-Generated Target
In certain training paradigms, the learning target is generated by the model itself rather than being a fixed ground-truth label. First, an optimal prediction, , is determined, often by maximizing a log-probability function. This prediction is then used as the target for learning. The loss function is subsequently defined as the log-probability of this model-generated target, conditioned on variables such as a modified context and a latent variable . The formula is: This objective is typically maximized during training, which is equivalent to minimizing its negative.
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Computing Sciences
Foundations of Large Language Models Course
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Log-Probability Loss with Model-Generated Target
A research team is training a generative model using a method where the learning target for any given input is the output that the model itself currently calculates as having the highest probability. This self-generated target is then used to update the model's parameters. Which statement best analyzes a key implication of this training approach?
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Analysis of a Self-Supervised Training Strategy
A model is trained using a two-stage process. In the first stage, given an input context
c, the model identifies an optimal output sequence,ŷ. In the second stage, the model's parameters are updated to maximize the probability of generating that same sequenceŷ, but this time conditioned on a slightly modified version of the original context,c'. What is the primary reason for using the modified contextc'in the second stage instead of the original contextc?Consider a training process where the objective function is defined as
Loss = log Pr(ŷ | c', z), withŷbeing an optimal prediction generated by the model itself. During training, the model's parameters are updated with the goal of minimizing this specificLossvalue.