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Related Questions
- What are the common techniques used to adapt transfer learning-based text generation models to out-of-domain data?
- How can domain adaptation methods, such as multi-task learning and adversarial training, be applied to improve model performance on out-of-domain data?
- What is the role of data augmentation and data selection in adapting transfer learning-based text generation models to out-of-domain data?
- Can you explain the concept of meta-learning and how it can be used to adapt transfer learning-based text generation models to new domains?
- How can transfer learning-based text generation models be fine-tuned on out-of-domain data to maintain their performance?
- What are the challenges and limitations of adapting transfer learning-based text generation models to out-of-domain data, and how can they be addressed?
- Can you discuss the importance of data quality and quantity in adapting transfer learning-based text generation models to out-of-domain data?
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