AI0-001 AI Concepts and Techniques Practice Question
A team is fine-tuning a BERT model for a document classification task. They notice the model achieves high F1 scores on the training set but low F1 on the validation set. Which regularization technique would be MOST effective?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Dropout
Dropout randomly drops neurons during training, preventing co-adaptation and overfitting. L1 and L2 add penalties to weights but are less common for transformers; L1 induces sparsity, L2 reduces weight magnitude. However, dropout is the standard regularization in BERT-like models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
L1 regularization
Why it's wrong here
L1 adds a penalty on absolute weights, promoting sparsity, but is less effective than dropout for transformers.
- ✗
L2 regularization
Why it's wrong here
L2 penalizes large weights, but dropout is more commonly used in transformer fine-tuning to reduce overfitting.
- ✓
Dropout
Why this is correct
Dropout is widely used in transformer models; increasing dropout rate during fine-tuning can reduce overfitting.
- ✗
Reduce batch size
Why it's wrong here
Reducing batch size can introduce noise but is not a regularization technique per se and may not directly address overfitting.
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