1Z0-1127-25 LLM Fundamentals Practice Question
A data scientist is fine-tuning a Llama 2 7B model on a custom dataset using OCI Data Science. After training, the model generates fluent but factually incorrect statements about the new domain. Which post-training technique would BEST address this issue without retraining?
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
✓
Use a retrieval-augmented generation (RAG) pipeline
RAG retrieves factual information from an external knowledge base to ground the generation, reducing hallucinations. The other options do not address factual accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the temperature to 0.1
Why it's wrong here
Lower temperature makes output more deterministic but does not inject new facts.
- ✗
Switch to a larger model like Llama 2 70B
Why it's wrong here
Larger models may still hallucinate; RAG is a more direct solution.
- ✗
Apply top-p sampling with p=0.9
Why it's wrong here
Top-p sampling affects creativity, not factual grounding.
- ✓
Use a retrieval-augmented generation (RAG) pipeline
Why this is correct
RAG retrieves relevant documents and feeds them as context, reducing hallucinations by grounding responses in verified sources.
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