NCA-GENL Question 4
Select 4A data scientist is training a language model for a sentiment analysis task using a dataset of labeled reviews. They decide to use a transformer-based architecture and train the model from scratch. However, the training does not converge properly. Which of the following techniques could help improve the model's performance?
- A
Use a pre-trained transformer model and fine-tune it on the sentiment analysis dataset
- B
Increase the size of the labeled dataset by collecting more training examples
- C
Reduce the learning rate and increase the number of training epochs
- D
Replace the transformer-based architecture with a simpler model such as Logistic Regression
- E
Add dropout layers to prevent overfitting during training
Show answer and explanation
Correct answers: A, B, C, E
Explanation
Training a transformer-based language model from scratch requires significant data and computational resources. Fine-tuning a pre-trained model can save time and improve convergence by leveraging knowledge from prior tasks. Increasing the dataset size and adjusting hyperparameters like the learning rate can help stabilize training. Dropout layers are useful for mitigating overfitting, but replacing the architecture with a simpler model would likely degrade performance in this scenario.
- A. Correct.
Using a pre-trained transformer model and fine-tuning it on the specific task is a common and effective technique in transfer learning. It allows the model to leverage prior knowledge while adapting to the new task.
- B. Correct.
Increasing the size of the labeled dataset provides more examples for the model to learn from, improving its ability to generalize.
- C. Correct.
Reducing the learning rate and increasing the number of training epochs can stabilize training and allow the model to converge more effectively.
- D. Incorrect.
Replacing the transformer-based architecture with a simpler model might reduce complexity, but it is unlikely to achieve comparable performance on a complex NLP task like sentiment analysis.
- E. Correct.
Adding dropout layers helps prevent overfitting by randomly deactivating neurons during training, which can improve generalization.