NCA-GENM Question 100
Select 3You are developing a multimodal generative AI model that processes both text and image data. During the testing phase, you notice inconsistencies in the results when combining these modalities. Which of the following steps would help ensure the quality and consistency of the test data in this multimodal setting?
- A
Ensure that the text and image inputs are semantically aligned in the test dataset.
- B
Verify that the image data is in a consistent format and resolution across the dataset.
- C
Use only text data to simplify testing and avoid inconsistencies between modalities.
- D
Check for class imbalance in the test dataset to ensure fair evaluation of the model.
- E
Randomly shuffle the test data to prevent overfitting during evaluation.
Show answer and explanation
Correct answers: A, B, D
Explanation
Testing data quality and consistency in a multimodal setting requires ensuring that the data from different modalities (e.g., text and images) is semantically aligned, properly formatted, and fairly distributed to provide a reliable evaluation of the model's performance. Ignoring or simplifying one of the modalities compromises the integrity of the multimodal model's testing process.
- A. Correct.
Ensuring semantic alignment between text and image inputs is essential for testing the multimodal model, as the modalities must complement each other to provide meaningful results.
- B. Correct.
Consistency in image format and resolution is critical to prevent errors during processing and to ensure fair evaluation of the model's performance.
- C. Incorrect.
Using only text data disregards the multimodal nature of the model and does not address the inconsistencies between the modalities.
- D. Correct.
Checking for class imbalance ensures that the model is evaluated fairly across all categories, which is necessary for a reliable assessment of its performance.
- E. Incorrect.
Randomly shuffling the test data does not address the root cause of inconsistencies and is not a method for improving test data quality or consistency.