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1Z0-1127-25 LLM Fundamentals Practice Question

A data scientist is evaluating a summarization model on a news article dataset. They compute ROUGE-L and BLEU scores. The ROUGE-L score is high, but the BLEU score is low. Which of the following best explains this discrepancy?

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 summaries are very short and use different vocabulary from the reference

ROUGE-L measures the longest common subsequence, emphasizing recall of key content (n-grams in order). BLEU measures n-gram precision, penalizing short or non-fluent outputs. A high ROUGE-L but low BLEU suggests the summaries capture the main ideas but use different phrasing or are too short.

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 BLEU score is unreliable for summarization tasks
Why it's wrong here
While BLEU is more suited to translation, it is still used in summarization; the discrepancy is better explained by the nature of the summaries.
✗
The model is overfitting to the training data
Why it's wrong here
ROUGE and BLEU are both computed on held-out test data; overfitting would likely affect both scores similarly, not produce this discrepancy.
✓
The summaries are very short and use different vocabulary from the reference
Why this is correct
Short summaries that capture key content (high ROUGE-L) but with different word choices (low n-gram overlap) cause low BLEU.
✗
The summaries are too long and contain many irrelevant n-grams
Why it's wrong here
Long summaries would likely increase BLEU precision unless they use unusual n-grams; this does not explain low BLEU with high ROUGE-L.

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1Z0-1127-25 LLM Fundamentals Practice Question

Why each option matters

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