AIF-C01 Question 152
Select 3You are working with a foundation model deployed on AWS that generates text summaries for documents. To evaluate its performance, you are tasked with assessing the quality and accuracy of the model's outputs. Which of the following approaches would be most appropriate for evaluating the model's performance?
- A
Manually reviewing the model's outputs for accuracy and coherence.
- B
Comparing the model's outputs against a benchmark dataset with pre-labeled summaries.
- C
Analyzing the GPU utilization during inference to estimate model efficiency.
- D
Using automated metrics such as BLEU or ROUGE scores to measure text similarity.
- E
Monitoring user click-through rates after deploying the model in production.
Show answer and explanation
Correct answers: A, B, D
Explanation
Evaluating a foundation model's performance often involves combining human evaluation, benchmark datasets, and automated metrics. Human evaluation ensures subjective qualities like coherence and relevance are assessed, while benchmark datasets and automated metrics provide objective, repeatable measurements. Analyzing GPU utilization and monitoring user click-through rates are not directly relevant to model performance evaluation.
- A. Correct.
Manually reviewing the model's outputs involves human evaluation, which is effective for assessing subjective qualities like coherence, readability, and accuracy. This is a valid approach for evaluating foundation model performance.
- B. Correct.
Comparing against a benchmark dataset allows for objective measurement by checking the model's outputs against pre-defined, high-quality references. This is an industry-standard evaluation approach.
- C. Incorrect.
Analyzing GPU utilization is unrelated to evaluating the quality or performance of the foundation model's outputs. It is more relevant for infrastructure or cost optimization.
- D. Correct.
Automated metrics like BLEU or ROUGE are widely used for comparing text-based model outputs to reference texts. These metrics provide objective measures of similarity and are valid for evaluating model performance.
- E. Incorrect.
Monitoring user click-through rates is a post-deployment metric that measures user behavior, not the intrinsic performance or quality of the model's outputs.