1Z0-1127-25 Fundamentals of Large Language Models Practice Question
During fine-tuning of a large language model on OCI, you notice that the model's performance on the validation set is not improving after several epochs, but the training loss continues to decrease. What is the most likely cause?
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
✓
The model is overfitting to the training data.
When training loss decreases but validation performance stagnates or worsens, the model is overfitting to the training data. It memorizes the training examples but fails to generalize. A high learning rate might cause divergence, not this pattern. Too small training data can contribute to overfitting but is not the direct symptom. An unrepresentative validation set could cause mismatch, but the described pattern is classic overfitting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The learning rate is too high.
Why it's wrong here
A high learning rate typically causes divergent behavior and both losses would increase or oscillate, not the pattern described.
- ✗
The validation set is not representative.
Why it's wrong here
An unrepresentative validation set could show poor performance even with good generalization, but the training loss decrease is consistent with overfitting, not just a mismatch.
- ✓
The model is overfitting to the training data.
Why this is correct
Overfitting occurs when the model memorizes training examples, causing training loss to drop while validation performance plateaus or declines. This is the most likely cause.
- ✗
The training data is too small.
Why it's wrong here
Small data can lead to overfitting, but the immediate symptom of training loss decreasing while validation does not improve is directly indicative of overfitting, not necessarily the root cause.
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