A company is evaluating several large language models (LLMs) for a text summarization task. The company needs to select a metric to evaluate the quality of the summaries that the LLMs generate…

The correct answer is C. Recall-Oriented Understudy for Gisting Evaluation (ROUGE). The ROUGE metric is widely used to evaluate the quality of summaries generated by language models, as it measures the overlap between the generated summary and reference summaries in terms of recall, precision, and F1-score for n-grams and sequences.

Submitted by salim_om· Mar 30, 2026Fundamentals of AI and ML

Question

A company is evaluating several large language models (LLMs) for a text summarization task. The company needs to select a metric to evaluate the quality of the summaries that the LLMs generate. Which metric will meet this requirement?

Options

ARecall
BArea under the ROC curve (AUC)
CRecall-Oriented Understudy for Gisting Evaluation (ROUGE)
DMean squared error (MSE)

How the community answered

(34 responses)

A
3% (1)
B
6% (2)
C
88% (30)
D
3% (1)

Explanation

The ROUGE metric is widely used to evaluate the quality of summaries generated by language models, as it measures the overlap between the generated summary and reference summaries in terms of recall, precision, and F1-score for n-grams and sequences.

Topics

#ROUGE metric#text summarization#LLM evaluation#NLP metrics

Community Discussion

No community discussion yet for this question.

Full AIF-C01 Practice