NCA-GENM Question 27
Select 3You are designing a statistical analysis framework to evaluate the performance of a multimodal AI pipeline that integrates text and image data. Which of the following methods would be most appropriate for analyzing the alignment between the modalities and assessing the overall pipeline performance?
- A
Compute a correlation coefficient to measure the relationship between features extracted from text and image embeddings.
- B
Use confusion matrices to evaluate the accuracy of text-to-image retrieval tasks.
- C
Apply BLEU or ROUGE scores to evaluate the semantic similarity between text and generated image captions.
- D
Perform an ANOVA test to statistically compare the performance of multiple multimodal models.
- E
Calculate the Mean Squared Error (MSE) to assess the alignment between the text and image data.
Show answer and explanation
Correct answers: A, C, D
Explanation
Evaluating multimodal pipelines requires a combination of statistical methods tailored to the specific tasks being assessed. Correlation coefficients help analyze the relationship between features from different modalities. BLEU or ROUGE scores are effective for text-related evaluations (e.g., captions). ANOVA is valuable for comparing multiple models. However, confusion matrices and MSE are not directly relevant to most multimodal evaluation scenarios.
- A. Correct.
Correlation coefficients are suitable for assessing the degree of alignment between features extracted from text and image embeddings, which is crucial for evaluating their relationship in multimodal pipelines.
- B. Incorrect.
Confusion matrices are generally used for classification tasks, but they are not directly applicable to evaluating multimodal tasks like text-to-image retrieval.
- C. Correct.
BLEU and ROUGE scores are common metrics for evaluating text-related tasks, such as assessing how well generated captions align semantically with input text. This is relevant for multimodal evaluation.
- D. Correct.
ANOVA is a statistical test for comparing group means, and it is appropriate for comparing the performance of different multimodal models under the same conditions.
- E. Incorrect.
MSE is typically used for regression tasks, and while it measures error, it is not well-suited for assessing multimodal alignment directly.