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Related Questions
- What techniques do pre-trained language models use to regularize their weights and prevent overfitting?
- How do pre-trained language models handle the problem of overfitting when dealing with limited labeled data?
- Can pre-trained language models learn feature representations that are useful for downstream tasks even with limited labeled data?
- What are some common heuristics used to balance the trade-off between performance on the training set and generalization to new unseen data?
- Can pre-trained language models exploit the inductive bias provided by the large-scale, unlabeled data used in their pre-training?
- How do pre-trained language models handle the bias-variance trade-off, especially when dealing with a small amount of labeled data?
- Can fine-tuning pre-trained language models on a small, labeled dataset help alleviate overfitting, and what are the key factors influencing this process?
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