NCA-GENM Question 157
Select 3You are tasked with optimizing a generative AI multimodal model that processes both text and images to improve its accuracy and computational efficiency. Which of the following techniques would be the most effective in achieving this goal?
- A
Implementing mixed precision training to reduce computational overhead
- B
Fine-tuning the pre-trained model on a smaller, domain-specific dataset
- C
Increasing the batch size during training to improve convergence speed
- D
Using sparse attention mechanisms to focus on relevant parts of the input
- E
Adding more layers to the model to enhance its capacity to learn
Show answer and explanation
Correct answers: A, B, D
Explanation
To enhance computational efficiency and improve accuracy in AI models, techniques like mixed precision training reduce computation without sacrificing performance, fine-tuning adapts the model to specific tasks with fewer resources, and sparse attention mechanisms focus processing power on the most critical input regions. These strategies optimize both resource use and model output quality, making them ideal for multimodal generative AI tasks.
- A. Correct.
Mixed precision training uses lower-precision data types where possible, which reduces computational overhead and accelerates training without significantly impacting accuracy.
- B. Correct.
Fine-tuning on a smaller, domain-specific dataset allows the model to adapt to specific tasks, improving accuracy while using fewer computational resources compared to training from scratch.
- C. Incorrect.
While increasing the batch size may speed up convergence in some cases, it does not directly improve accuracy or computational efficiency and may require more memory.
- D. Correct.
Sparse attention mechanisms reduce computational cost by focusing on the most relevant parts of the input, which enhances both efficiency and accuracy in processing multimodal data.
- E. Incorrect.
Adding more layers increases the model size, which may improve learning capacity but often leads to higher computational costs and potential overfitting, making it less efficient.