1Z0-1127-25 LLM Fundamentals Practice Question
A researcher wants to compare two summarization models. Model A achieves a higher ROUGE-L score than Model B, but human evaluators prefer Model B's summaries. Which of the following is the MOST likely reason?
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
✓
ROUGE-L measures n-gram overlap, which may not align with human judgment of quality
ROUGE-L measures n-gram overlap, which may not capture semantic quality. Human evaluators often prefer summaries that are fluent, coherent, and concise, even if they use different wording. The discrepancy indicates that ROUGE-L alone is insufficient for evaluation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Model A overfits to the training data
Why it's wrong here
ROUGE-L is computed on a test set; overfitting would not explain human preference for Model B.
- ✗
Model B has a larger context window
Why it's wrong here
Context window size is not directly related to summarisation quality comparison here.
- ✓
ROUGE-L measures n-gram overlap, which may not align with human judgment of quality
Why this is correct
Human evaluators consider factors like readability and conciseness, which ROUGE-L does not capture fully.
- ✗
Model A is an encoder-decoder model while Model B is decoder-only
Why it's wrong here
Architecture type alone does not determine human preference; both architectures can produce good summaries.
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