1Z0-1127-25 LLM Fundamentals Practice Question
A researcher is evaluating two LLMs for a summarization task. Model A achieves a ROUGE-L score of 0.45 and a BERTScore of 0.92. Model B achieves a ROUGE-L score of 0.50 and a BERTScore of 0.88. Which model is likely better for producing summaries that are semantically faithful to the source, even if not using the exact same words?
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
✓
Model A because BERTScore is higher
BERTScore measures semantic similarity using contextual embeddings, while ROUGE-L measures n-gram overlap. Higher BERTScore indicates better semantic faithfulness even without exact phrase matches.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Neither model is acceptable because ROUGE-L is below 0.6
Why it's wrong here
There is no universal threshold; model selection depends on the specific task requirements.
- ✗
Both are equally good because the scores are close
Why it's wrong here
The scores are not equally good; the choice depends on the evaluation priority. For semantic faithfulness, BERTScore is more relevant.
- ✗
Model B because ROUGE-L is higher
Why it's wrong here
ROUGE-L rewards exact n-gram overlap, which does not necessarily correlate with semantic faithfulness.
- ✓
Model A because BERTScore is higher
Why this is correct
Higher BERTScore suggests better semantic alignment with the source, which is more important for faithfulness.
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