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Related Questions
- What are common causes of domain shift in transfer learning, and how can they be identified?
- How can domain adaptation techniques, such as data augmentation and adversarial training, be applied to mitigate the effects of domain shift?
- What is the role of meta-learning in addressing domain shift, and how can it be incorporated into a large language model?
- Can you explain the concept of domain-invariant features and how they can be used to reduce the impact of domain shift?
- How can the performance of a large language model be evaluated in a domain-shifted scenario using metrics such as accuracy, F1-score, and ROC-AUC?
- What is the relationship between domain shift and overfitting, and how can regularization techniques be used to address both issues?
- How can transfer learning be used to adapt a large language model to a new domain, and what are some common challenges that arise during this process?
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