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Fundamentals of Large Language ModelsmediumMultiple SelectObjective-mapped

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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