NCA-GENM Question 98
Select 3A data scientist is building a sentiment analysis system for a customer service chatbot. They want to enhance the system's explainability to build user trust. The system currently processes both text and images sent by users. Which of the following approaches using multimodal models would most effectively improve explainability?
- A
Use a multimodal attention mechanism to highlight relevant text and image regions contributing to the sentiment classification.
- B
Generate visual explanations by overlaying attention heatmaps on the input images to show areas of focus.
- C
Train separate text and image models independently and combine their outputs without explaining the fusion process.
- D
Use a text-only model to simplify the system and avoid dealing with image data.
- E
Incorporate a natural language explanation generator to describe how text and image inputs influence the prediction.
Show answer and explanation
Correct answers: A, B, E
Explanation
To improve explainability in multimodal models, it is essential to leverage tools like attention mechanisms, visual explanations, and natural language explanations. These approaches enhance transparency by clarifying how the model processes and integrates text and image inputs for predictions. Simplifying the system or ignoring the fusion process would compromise the benefits of multimodal explainability.
- A. Correct.
This is correct because multimodal attention mechanisms help in identifying which parts of the text and image inputs contribute to the prediction, improving transparency.
- B. Correct.
This is correct as visual explanations, such as attention heatmaps, make it easier for users to understand how the model uses image data for predictions.
- C. Incorrect.
This is incorrect because combining outputs from separate models without explaining the fusion process does not improve explainability.
- D. Incorrect.
This is incorrect as using a text-only model sacrifices multimodal input processing, which is critical for explainability in this context.
- E. Correct.
This is correct since generating natural language explanations provides an intuitive way for users to understand the contribution of both text and image inputs to the model's decision.