AWS Certified AI Practitioner AIF-C01 Practice Question
After fine-tuning a foundation model for text summarization, which evaluation method gives the most objective and repeatable comparison of summarization quality between the fine-tuned and base models?
Compare the final training loss values recorded during fine-tuning.
Count the total number of tokens produced in a set of summaries.
Calculate ROUGE scores on a held-out summarization benchmark dataset.
Have an internal reviewer subjectively rate a few example summaries.
Automatic metrics such as ROUGE compare a model's generated summaries against reference summaries across an entire benchmark dataset, producing quantitative scores that can be rerun and replicated. Token counts do not measure quality, training loss reflects how well the model fits the training data rather than summary quality, and ad-hoc human review is subjective and hard to reproduce.
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What is the ROUGE metric and why is it used for evaluating text summarization?
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Why are token counts not a valid measure of text summarization quality?
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What are the limitations of using training loss as an evaluation method for summarization?
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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