NCA-GENM Question 156
Select 3You are tasked with optimizing a multimodal generative AI model to improve its computational efficiency and accuracy. Which of the following techniques would be the most appropriate for achieving this goal?
- A
Implementing model quantization to reduce the precision of weights and activations.
- B
Using knowledge distillation to transfer knowledge from a larger model to a smaller model.
- C
Increasing the number of training epochs without adjusting the learning rate.
- D
Incorporating early stopping to prevent overfitting during training.
- E
Adding more layers to the model to enhance its representational capability.
Show answer and explanation
Correct answers: A, B, D
Explanation
Enhancing computational efficiency and improving accuracy in AI models often involves techniques like quantization, knowledge distillation, and early stopping. These methods either reduce computational load or prevent overfitting, making them effective for optimization. On the other hand, increasing the number of epochs without proper adjustments or adding more layers can lead to inefficiencies and potentially degrade model performance.
- A. Correct.
Implementing model quantization can significantly reduce the computational requirements of the model while maintaining an acceptable level of accuracy, thus enhancing efficiency.
- B. Correct.
Knowledge distillation enables a smaller model (student) to learn from a larger, well-trained model (teacher), which improves efficiency and maintains the accuracy of outputs.
- C. Incorrect.
Simply increasing the number of training epochs without adjusting the learning rate can lead to overfitting and does not directly improve computational efficiency.
- D. Correct.
Early stopping helps in preventing overfitting and reduces unnecessary computation, which improves both computational efficiency and the accuracy of the final model.
- E. Incorrect.
Adding more layers to the model increases its computational complexity and may not necessarily improve accuracy, especially if the model is already well-tuned.