1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A data scientist is evaluating different models for a summarization task. Which two metrics are commonly used to evaluate the quality of generated summaries?
⚠ Common exam trap
Oracle often tests the distinction between metrics used for summarization (ROUGE) versus translation (BLEU) versus language modeling (Perplexity), and candidates may confuse BLEU as a summarization metric because it also evaluates text generation, but it is primarily designed for translation tasks.
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 (Recall-Oriented Understudy for Gisting Evaluation) is a standard metric for summarization that measures the overlap of n-grams, word sequences, or word pairs between the generated summary and reference summaries. It focuses on recall, making it well-suited for evaluating how well the generated summary captures the key content from the reference.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
F1 score
Why it's wrong here
F1 is typically used for classification tasks, not for evaluating text generation quality.
- ✗
Mean Average Precision
Why it's wrong here
MAP is used for information retrieval and ranking, not for summarization.
- ✓
ROUGE
Why this is correct
ROUGE measures overlap of n-grams between generated and reference summaries, commonly used for summarization.
- ✗
Perplexity
Why it's wrong here
Perplexity measures how well a model predicts a sample, but it is not a direct evaluation of summary quality against references.
- ✓
BLEU
Why this is correct
BLEU measures precision of n-gram overlap and is widely used for text generation tasks including summarization.
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