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You are fine-tuning a pre-trained language model for a specific task. You notice that the model performs well on the training data but poorly on the validation dat a. Which of the following techniques can help mitigate this overfitting problem? (Select TWO)
Correct Answer: B,C
Overfitting occurs when a model learns the training data too well and fails to generalize to new data. Weight decay (L2 regularization) penalizes large weights, preventing the model from becoming too complex. Dropout randomly deactivates neurons during training, forcing the model to learn more robust features. Increasing the learning rate might worsen overfitting. Decreasing the batch size can sometimes act as a regularizer, but its primary effect is on the training dynamics. While more training data is generally beneficial, if the new data is very similar to the existing training data, it won't necessarily solve the overfitting issue.