1Z0-1127-25 LLM Fundamentals Practice Question
Which of the following metrics is most suitable for evaluating a translation model's output against multiple reference translations?
⚠ Common exam trap
Many exam-takers confuse ROUGE (recall-based) with BLEU (precision-based) or assume BERTScore's semantic matching is better for translation, but BLEU is explicitly the standard for multi-reference translation evaluation in the NLP community.
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
BLEU (Bilingual Evaluation Understudy) is the most suitable metric for evaluating a translation model's output against multiple reference translations because it measures n-gram precision between the candidate translation and one or more reference translations. It directly quantifies how many words and phrases in the candidate match those in the references, making it the standard metric for machine translation tasks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
ROUGE
Why it's wrong here
ROUGE is primarily for summarization.
- ✗
Perplexity
Why it's wrong here
Perplexity measures language model confidence, not translation quality.
- ✗
BERTScore
Why it's wrong here
BERTScore is semantic but not the standard for translation; BLEU is more common.
- ✓
BLEU
Why this is correct
BLEU is the standard metric for machine translation.
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