1Z0-1127-25 LLM Fundamentals Practice Question
A machine learning engineer is evaluating the performance of a translation model using BLEU score. Which THREE statements about BLEU are correct? (Choose three.)
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
✓
BLEU includes a brevity penalty to penalize outputs that are too short
BLEU is a precision-based metric (not recall). It uses modified n-gram precision with a brevity penalty. It correlates reasonably well with human judgment at the corpus level but has known limitations such as not capturing semantic equivalence.
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 includes a brevity penalty to penalize outputs that are too short
Why this is correct
The brevity penalty prevents short outputs from achieving artificially high scores.
- ✓
BLEU computes n-gram precision up to a maximum n (usually 4)
Why this is correct
BLEU typically uses n-gram precision for n=1 to n=4.
- ✓
BLEU correlates well with human judgment at the corpus level
Why this is correct
At the corpus level, BLEU has shown reasonable correlation with human evaluations, though it has limitations at the sentence level.
- ✗
BLEU measures recall of n-grams by comparing the output to the reference
Why it's wrong here
BLEU measures precision, not recall. It calculates how many n-grams in the output appear in the reference.
- ✗
BLEU is a recall-oriented metric
Why it's wrong here
BLEU is precision-oriented; ROUGE is recall-oriented.
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