1Z0-1127-25 LLM Fundamentals Practice Question
A data scientist is evaluating an LLM's performance on a summarization task. They observe that the model produces fluent summaries but often misses key information. Which TWO metrics would best capture this issue? (Select TWO.)
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
✓
Human evaluation with a rubric for completeness
ROUGE-L measures recall of the longest common subsequence, capturing information coverage. Human evaluation can assess completeness. BLEU emphasizes precision and fluency. BERTScore measures semantic similarity but not directly the presence of key points. Perplexity measures model confidence, not recall.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
BLEU score
Why it's wrong here
BLEU emphasizes n-gram precision and fluency, not recall of key information.
- ✗
Perplexity
Why it's wrong here
Perplexity measures how well the model predicts the text, not the presence of key information.
- ✓
Human evaluation with a rubric for completeness
Why this is correct
Human judgment can directly assess whether key information is included.
- ✓
ROUGE-L
Why this is correct
ROUGE-L measures recall-oriented overlap, indicating how much key information from the source is retained.
- ✗
BERTScore
Why it's wrong here
BERTScore measures semantic similarity, but does not directly measure recall of specific key points.
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