NCA-GENM Question 108
Select 3A team is developing a multimodal generative AI model that combines text and image data to generate creative outputs. To ensure the model's accuracy and effectiveness, they need to evaluate it rigorously. Which of the following steps are essential for testing and validating the model?
- A
Use a diverse and representative validation dataset that covers all input modalities the model supports.
- B
Measure the model's performance only on the training dataset to confirm its accuracy.
- C
Evaluate the model's outputs using both quantitative metrics and human evaluation for subjective quality.
- D
Check for biases by analyzing how the model performs across different demographic or contextual scenarios.
- E
Test the model exclusively on high-quality inputs to simplify the evaluation process.
Show answer and explanation
Correct answers: A, C, D
Explanation
Testing generative AI models effectively requires using diverse datasets, combining quantitative and qualitative evaluation methods, and checking for biases to ensure fairness and generalizability. Avoiding overfitting to training data and ensuring the model performs well on real-world inputs are also key to validating its accuracy and effectiveness.
- A. Correct.
Using a diverse and representative validation dataset ensures that the model is tested on a wide range of scenarios and input types, which is critical for robust evaluation.
- B. Incorrect.
Measuring performance only on the training dataset can lead to overfitting and does not ensure generalizability to unseen data.
- C. Correct.
Evaluating the model with both quantitative metrics (e.g., BLEU scores, FID for images) and human evaluation provides a balanced assessment of its accuracy and subjective effectiveness.
- D. Correct.
Checking for biases is crucial in ensuring the model performs fairly and accurately across different scenarios, which improves its reliability and reduces unintended harm.
- E. Incorrect.
Testing exclusively on high-quality inputs does not reflect real-world usage, where inputs can vary in quality. This approach would lead to an incomplete evaluation.