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LLM FundamentalshardMultiple SelectObjective-mapped

1Z0-1127-25 LLM Fundamentals Practice Question

A team is evaluating two LLMs for a summarization task. Model X has a BERTScore of 0.85, Model Y has a BERTScore of 0.82. However, human evaluators prefer Model Y. Which TWO reasons could explain 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

BERTScore is based on BERT embeddings, which may not fully capture summary-specific qualities like conciseness or readability

BERTScore correlates with human judgment but is not perfect; it may favor certain styles. Additionally, BERTScore may be inflated if the reference summaries are similar to the model's training data.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • BERTScore is based on BERT embeddings, which may not fully capture summary-specific qualities like conciseness or readability

    Why this is correct

    BERTScore measures semantic similarity but may not reflect human preferences for style.

  • BERTScore uses precision only, so it misses recall aspects

    Why it's wrong here

    BERTScore combines precision and recall via F1.

  • Human evaluators were not given clear criteria for evaluation

    Why it's wrong here

    Unclear criteria could cause variability, but the discrepancy is systematic.

  • Model Y was fine-tuned on a different dataset, causing distribution shift

    Why it's wrong here

    Distribution shift would affect both metrics equally, not cause a discrepancy.

  • Model X overfits to the reference summaries, achieving high BERTScore but poor general quality

    Why this is correct

    Overfitting can inflate automatic metrics while reducing human-perceived quality.

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