1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A team wants to evaluate an LLM's performance on a text classification task. Which metric is most appropriate for a balanced dataset?
⚠ Common exam trap
Oracle often tests the distinction between metrics for generation tasks (BLEU, ROUGE, perplexity) versus classification tasks (accuracy, F1-score), and the trap here is assuming a language model metric like perplexity applies to any NLP task, when it is specific to probabilistic language modeling.
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
✓
Accuracy
Accuracy is the most appropriate metric for evaluating an LLM on a text classification task with a balanced dataset because it directly measures the proportion of correctly predicted labels out of total predictions. For balanced classes, accuracy provides a reliable and intuitive performance indicator without the distortion caused by class imbalance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
BLEU score
Why it's wrong here
BLEU is for machine translation evaluation.
- ✗
Perplexity
Why it's wrong here
Perplexity evaluates language model fluency, not classification accuracy.
- ✓
Accuracy
Why this is correct
Accuracy directly measures correct predictions, appropriate for balanced data.
- ✗
ROUGE score
Why it's wrong here
ROUGE is for summarization evaluation.
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