1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A researcher wants to compare the performance of two LLMs on OCI Generative AI: a base model and an instruct model. They notice the instruct model often refuses to generate certain types of content. Which factor most likely explains this behavior?
⚠ Common exam trap
Oracle often tests the misconception that refusal behavior is due to dataset size or rule-based programming, when in fact it is a direct result of RLHF-based safety alignment in instruct models.
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 instruct model has been fine-tuned with reinforcement learning from human feedback (RLHF) to align with safety guidelines.
Instruct models are typically fine-tuned using reinforcement learning from human feedback (RLHF) to align with safety guidelines and ethical constraints. This fine-tuning process teaches the model to refuse generating harmful, biased, or unsafe content, which explains why the instruct model refuses certain types of content while the base model does not.
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 base model was programmed to follow stricter rules.
Why it's wrong here
Base models do not have built-in refusal rules.
- ✓
The instruct model has been fine-tuned with reinforcement learning from human feedback (RLHF) to align with safety guidelines.
Why this is correct
RLHF makes instruct models more likely to reject unsafe requests.
- ✗
The instruct model was trained on a smaller dataset.
Why it's wrong here
Instruct models are usually fine-tuned on additional data, not smaller.
- ✗
The base model rejects content more often.
Why it's wrong here
Base models typically have fewer guardrails.
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