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Related Questions
- What are the key differences between domain adaptation and transfer learning in text generation?
- How does domain adaptation enable models to learn domain-specific language patterns and nuances?
- What are some common techniques used in domain adaptation for text generation, such as data augmentation and task adaptation?
- Can you explain the concept of 'source' and 'target' domains in domain adaptation and how they impact model performance?
- How does domain adaptation address the issue of 'domain shift' in text generation, where models are trained on one domain but deployed in another?
- What are some real-world applications of domain adaptation in text generation, such as medical or financial text summarization?
- How can domain adaptation be used to improve the robustness and generalizability of text generation models in the face of changing user preferences or new data sources?
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