NCA-GENM Question 106
Select 3You are tasked with testing the accuracy and effectiveness of a generative AI multimodal model designed to generate captions for images. Which of the following steps should you include in your testing process to ensure the model performs as expected?
- A
Evaluate the generated captions against a benchmark dataset using BLEU or CIDEr scores.
- B
Test the model on unseen data from a different domain to evaluate its generalization capability.
- C
Fine-tune the model further on the test dataset to improve its accuracy before testing.
- D
Manually review a sample of generated captions to evaluate contextual relevance and coherence.
- E
Ignore edge cases and focus on testing common scenarios for faster results.
Show answer and explanation
Correct answers: A, B, D
Explanation
Testing the accuracy and effectiveness of a generative AI multimodal model requires both quantitative and qualitative evaluations. Performance metrics like BLEU or CIDEr help in measuring accuracy against benchmarks, while manual reviews provide insights into contextual relevance and coherence. Additionally, testing on unseen data ensures the model is robust and generalizes well to new scenarios. Fine-tuning on test data compromises the integrity of the test, and ignoring edge cases limits the thoroughness of the evaluation.
- A. Correct.
Evaluating generated captions using metrics like BLEU or CIDEr provides a quantitative assessment of the model's performance by comparing its outputs to a benchmark dataset.
- B. Correct.
Testing the model on unseen data from a different domain helps assess its generalization capability, which is crucial for real-world applications.
- C. Incorrect.
Fine-tuning the model on the test dataset invalidates the testing process, as the model would have already seen the data, leading to biased results.
- D. Correct.
Manually reviewing a sample of generated captions allows for qualitative evaluation of aspects like contextual relevance, coherence, and creativity, complementing quantitative metrics.
- E. Incorrect.
Ignoring edge cases reduces the comprehensiveness of the test, as edge cases often reveal critical weaknesses in the model.