NCA-GENL Question 145
Select 3You are tasked with developing and testing a custom fine-tuned language model using NVIDIA's NeMo framework. During the process, you notice that the model's accuracy on validation data has plateaued despite extensive training. What steps should you take to address this issue?
- A
Reduce the learning rate to allow for finer weight updates.
- B
Increase the size of the training dataset by using data augmentation techniques.
- C
Switch to a completely different training framework to restart the process.
- D
Monitor the gradient norms to check for potential gradient vanishing or exploding issues.
- E
Enable mixed precision training to improve computational performance.
Show answer and explanation
Correct answers: A, B, D
Explanation
When a model's accuracy plateaus, the objective is to identify and address potential limitations in the training process or data. Reducing the learning rate can help fine-tune the model's weights, adding more data can improve generalization, and monitoring gradient norms can diagnose critical issues in the training process. These steps are directly related to optimizing and maintaining the model's performance, which is crucial for working with NVIDIA's NeMo framework.
- A. Correct.
Reducing the learning rate can help the model converge more precisely towards a minimum, especially when progress has plateaued.
- B. Correct.
Increasing the size of the training dataset or augmenting it can provide the model with more diverse patterns to learn and improve its generalization.
- C. Incorrect.
Switching to a different framework does not directly address the issue and is unlikely to resolve the plateau in accuracy.
- D. Correct.
Monitoring gradient norms can help diagnose issues such as gradient vanishing or exploding, which can hinder the training process.
- E. Incorrect.
While mixed precision training improves computational performance, it does not directly address the accuracy plateau or improve the model's ability to generalize.