NCA-GENM Question 104
Select 4You are tasked with testing a multimodal generative AI model designed to generate image captions. Which of the following methods would help ensure the model's accuracy and effectiveness?
- A
Evaluate the model using BLEU or METEOR scores to compare generated captions with ground truth captions.
- B
Test the model by generating captions for unseen images and checking them manually for relevance and correctness.
- C
Use adversarial input images to identify failure cases and analyze how the model performs under challenging conditions.
- D
Measure the model's training loss to determine its effectiveness at generating captions.
- E
Assess the model's performance on a diverse dataset that includes different styles, objects, and lighting conditions.
Show answer and explanation
Correct answers: A, B, C, E
Explanation
Testing multimodal AI models involves a combination of automated metrics (e.g., BLEU, METEOR), manual evaluation, adversarial testing, and scenario diversity checks to ensure the model's accuracy, generalization, and robustness. Training loss is only indicative of how well the model fits the training data and is not a sufficient measure of its real-world performance.
- A. Correct.
BLEU and METEOR scores are standard metrics for evaluating text generation models, such as image captioning systems. They help quantify how well the generated captions align with the ground truth.
- B. Correct.
Manually reviewing captions for unseen images verifies the model's ability to generalize to new data, which is critical for testing its accuracy and effectiveness.
- C. Correct.
Adversarial testing helps identify edge cases and robustness issues in the model, providing insights into how it performs under less-than-ideal conditions.
- D. Incorrect.
Training loss is a useful metric during model development but does not directly measure the model's effectiveness in generating accurate and meaningful captions for real-world use cases.
- E. Correct.
Evaluating performance on a diverse dataset ensures the model is robust and effective across a wide variety of scenarios, improving its real-world applicability.