1Z0-1127-25 OCI Generative AI Service Practice Question
During a fine-tuning job for a text generation model, the loss curve shows that the training loss decreases steadily, but the evaluation loss increases after a few epochs. Which action is most likely to improve the model's generalization?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Implement early stopping based on evaluation loss
Increasing evaluation loss while training loss decreases indicates overfitting. Early stopping prevents further training once evaluation metrics plateau or degrade, improving generalization. Reducing dataset size or increasing epochs would worsen overfitting; increasing temperature does not affect training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement early stopping based on evaluation loss
Why this is correct
Early stopping halts training when evaluation loss starts increasing, preventing overfitting.
- ✗
Increase the number of training epochs
Why it's wrong here
More epochs would continue to overfit, further increasing evaluation loss.
- ✗
Reduce the size of the training dataset
Why it's wrong here
Reducing training data typically worsens overfitting as the model has fewer examples to learn from.
- ✗
Increase the temperature parameter during generation
Why it's wrong here
Temperature is an inference parameter, not a training hyperparameter; it does not address overfitting.
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