NCA-GENM Question 155
Select 3A company is training a multimodal generative AI model that processes both text and images. The model requires significant computational resources, and the current outputs occasionally lack accuracy in aligning text descriptions with the corresponding images. Which of the following strategies would help enhance computational efficiency and improve the accuracy of the model's outputs?
- A
Use mixed-precision training to reduce computation time while maintaining model accuracy.
- B
Incorporate pre-trained embeddings for both text and image inputs to leverage existing knowledge.
- C
Increase the batch size significantly to process more data in parallel.
- D
Implement model pruning to remove redundant parameters without compromising performance.
- E
Switch to a simpler model architecture to reduce computational costs.
Show answer and explanation
Correct answers: A, B, D
Explanation
Enhancing computational efficiency and improving accuracy in AI models can be achieved through techniques like mixed-precision training, leveraging pre-trained embeddings, and using model pruning. These approaches optimize resource usage and improve performance without significantly compromising output quality. However, increasing batch size or switching to simpler architectures may not address both computational efficiency and accuracy requirements effectively.
- A. Correct.
Mixed-precision training reduces the computational resources required by using lower precision (e.g., FP16) for computations while still maintaining overall accuracy. This improves efficiency without a significant loss in performance.
- B. Correct.
Using pre-trained embeddings for text and images allows the model to leverage knowledge learned from large datasets, improving accuracy without requiring additional computational resources.
- C. Incorrect.
Increasing the batch size significantly might improve training speed in certain cases, but it often leads to memory bottlenecks and may not directly improve model accuracy.
- D. Correct.
Model pruning removes redundant or less significant parameters, which can reduce computational load and memory usage while maintaining or even improving accuracy.
- E. Incorrect.
Switching to a simpler model architecture reduces computational costs, but it may come at the expense of accuracy, especially for complex multimodal tasks.