NCA-GENM Exam Dumps | You are fine-tuning a pre-trained large language model (LLM) for a specific text generation task. During

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Question 109/192

You are fine-tuning a pre-trained large language model (LLM) for a specific text generation task. During training, you observe that the model is overfitting to the training data and not generalizing well to unseen examples. Which of the following techniques could be MOST effective in mitigating overfitting in this scenario?

A. Increasing the size of the training dataset.

B. Applying dropout regularization to the LLM's layers.

C. Decreasing the learning rate during fine-tuning.

D. Using a smaller batch size during fine-tuning.

E. Early stopping based on a validation set.

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