NCA-GENM Question 86
Single answerYou are part of a team developing a multimodal AI model that combines text and image inputs to generate detailed product descriptions for an e-commerce platform. During testing, the model produces inconsistent descriptions for similar images with identical text inputs. What is the most likely cause of this issue, and how should you address it?
- A
The model’s image encoder is not properly trained, and fine-tuning it on domain-specific data may improve performance.
- B
The text encoder is overfitting, and reducing the model’s training epochs will resolve the issue.
- C
The multimodal fusion mechanism is improperly configured, and adjustments to the attention mechanism could improve consistency.
- D
The training dataset is too large, and reducing its size will improve the model’s reliability.
Show answer and explanation
Correct answer: A
Explanation
Inconsistent outputs for similar images with identical text inputs often indicate that the image encoder is not extracting meaningful features for the specific use case. Fine-tuning the image encoder on domain-specific data ensures it learns features relevant to the e-commerce platform, improving the model’s performance. This aligns with best practices in developing and testing multimodal AI models where domain adaptation is crucial for effective feature extraction.
- A. Correct.
The issue is likely caused by the image encoder not being well-trained to extract meaningful features for the specific e-commerce domain. Fine-tuning the image encoder on domain-specific data can help improve the model’s consistency.
- B. Incorrect.
Overfitting in the text encoder is less likely to cause inconsistencies with similar text inputs and does not directly address the issue described.
- C. Incorrect.
While the multimodal fusion mechanism plays a role in combining text and image inputs, the described inconsistency is more likely to stem from the image encoder not extracting domain-relevant features.
- D. Incorrect.
Reducing the training dataset size will not address the underlying issue and could lead to underfitting, which would further degrade the model's performance.