1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A team is fine-tuning a large language model for a domain-specific Q&A application. After fine-tuning, they observe that the model performs well on the training distribution but struggles with out-of-distribution (OOD) questions. Which approach would best improve OOD robustness?
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
✓
Include a diverse set of examples from related domains in the fine-tuning dataset.
Including diverse examples from related domains (Option A) exposes the model to varied patterns, reducing overfitting to the training distribution and improving generalization to out-of-distribution inputs. Option B (early stopping) helps prevent overfitting on the validation set but does not specifically address OOD robustness. Option C (reducing model size) limits capacity, which can harm performance on both in-distribution and OOD data. Option D (increasing learning rate) risks catastrophic forgetting and training instability, not OOD robustness.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Include a diverse set of examples from related domains in the fine-tuning dataset.
Why this is correct
Diverse data improves generalization and OOD performance.
- ✗
Use early stopping based on training loss to avoid overfitting.
Why it's wrong here
Early stopping on training loss may not address OOD issues.
- ✗
Reduce the model size to prevent overfitting to the training data.
Why it's wrong here
Smaller model has less capacity to learn generalizable features.
- ✗
Increase the learning rate during fine-tuning to adapt faster to new patterns.
Why it's wrong here
Higher learning rate can cause instability and catastrophic forgetting.
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