1Z0-1127-25 LLM Fundamentals Practice Question
A researcher is comparing BLEU and ROUGE scores for a machine translation model. They notice that the BLEU score is high but the ROUGE score is low. Which scenario is MOST consistent with this observation?
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
✓
The model outputs concise translations that capture key words but miss some reference phrases
High BLEU and low ROUGE indicate that the generated text has high precision (many n-grams match reference) but low recall (missing many reference n-grams). This often occurs when the output is short or overly cautious.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model outputs very long and verbose translations
Why it's wrong here
Long outputs tend to have higher recall (ROUGE) and may have lower precision (BLEU).
- ✓
The model outputs concise translations that capture key words but miss some reference phrases
Why this is correct
Concise translations achieve high precision (high BLEU) but low recall (low ROUGE) because they miss some n-grams from the reference.
- ✗
The model is overfitting to the training data
Why it's wrong here
Overfitting may affect both metrics, not specifically this pattern.
- ✗
The reference translations are of poor quality
Why it's wrong here
Poor references affect both metrics, not just one.
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