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Related Questions
- What are the key differences between domain adaptation and transfer learning in the context of fine-tuning a text generation model?
- How can we balance the trade-off between domain-specific knowledge and language model generalizability during fine-tuning?
- What are some techniques for selecting the optimal subset of training data for fine-tuning a text generation model for a specific domain?
- Can you explain the concept of instance weighting and how it can be used to adapt a pre-trained language model to a specific domain?
- How does the choice of fine-tuning objective (e.g., masked language modeling, next sentence prediction) impact the model's generalizability?
- What are some strategies for regularizing the model during fine-tuning to prevent overfitting to the target domain?
- Can you discuss the role of data augmentation in fine-tuning a text generation model for a specific domain and how it can improve generalizability?
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