NCA-GENM Question 83
Select 3You are tasked with evaluating two multimodal AI models designed to perform text-to-image generation. Model A uses a transformer-based architecture, while Model B uses a hybrid approach combining convolutional neural networks (CNNs) with transformers. To determine which model performs better, you design an experiment using a benchmark dataset and specific metrics such as Fréchet Inception Distance (FID) and CLIP similarity scores. What factors should you consider during your evaluation to ensure fair and meaningful results?
- A
Ensure both models are trained on the same dataset and under the same conditions before evaluation.
- B
Use multiple evaluation metrics to account for different aspects of performance.
- C
Evaluate only on a single randomly selected subset of the dataset to save time.
- D
Consider the interpretability and computational efficiency of each model alongside performance metrics.
- E
Ignore qualitative human feedback as it introduces subjectivity.
Show answer and explanation
Correct answers: A, B, D
Explanation
To ensure a rigorous and fair evaluation of multimodal AI models, it is crucial to use consistent training conditions, multiple evaluation metrics, and consider both performance and practical aspects like interpretability and efficiency. While qualitative human feedback can be subjective, it should not be completely disregarded as it provides additional insight into the user experience.
- A. Correct.
Ensuring both models are trained on the same dataset and under the same conditions is critical to avoid bias and ensure a fair comparison.
- B. Correct.
Using multiple evaluation metrics, such as FID and CLIP scores, provides a comprehensive assessment of model performance across different dimensions like realism and relevance.
- C. Incorrect.
Evaluating on only a single subset of the dataset can lead to biased results and does not provide a representative measure of model performance.
- D. Correct.
Considering interpretability and computational efficiency is important as they impact real-world usability and deployment of the model.
- E. Incorrect.
Ignoring qualitative human feedback is not recommended, as it can provide valuable insights into user satisfaction and real-world applicability, despite its subjectivity.