AI / LLM Basics Interview Questions
What is the purpose of a Loss Function during LLM training?
A loss function gives training a single number representing how wrong the model's predictions currently are, which is what the training process actually tries to reduce.
- Computed after each batch of training examples, comparing the model's predictions against the correct answers
- Used to calculate gradients, which indicate how each of the model's billions of parameters should be adjusted
- Training repeats this cycle, predict, measure loss, adjust parameters, millions of times over the training data
Without a loss function, there would be no concrete signal telling the training process whether a given adjustment to the model actually made it better or worse.
What does a loss function provide during training?
What is calculated from the loss to adjust parameters?
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