1Z0-1127-25 LLM Fundamentals Practice Question
A data scientist is evaluating an LLM's performance on a summarization task. Which TWO metrics are most suitable for this evaluation?
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
✓
ROUGE
ROUGE measures recall-oriented overlap between generated and reference summaries, suitable for summarization. BERTScore uses semantic similarity via embeddings. BLEU is for translation, perplexity for language modeling, and human evaluation is qualitative.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Perplexity
Why it's wrong here
Perplexity measures how well a language model predicts a sequence, not directly evaluating summary quality.
- ✗
Human evaluation
Why it's wrong here
While valuable, human evaluation is not a metric in the traditional automated sense; it is subjective and resource-intensive.
- ✗
BLEU
Why it's wrong here
BLEU is primarily for machine translation, measuring precision of n-gram matches, not ideal for summarization.
- ✓
ROUGE
Why this is correct
ROUGE is specifically designed for summarization evaluation, measuring n-gram overlap and recall.
- ✓
BERTScore
Why this is correct
BERTScore computes semantic similarity using contextual embeddings, which can capture meaning beyond exact n-gram matches.
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