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Related Questions
- What is context-aware fine-tuning and how does it differ from other fine-tuning methods for domain adaptation?
- Can you explain the advantages of using context-aware fine-tuning for domain adaptation, such as improved adaptability and robustness?
- How does context-aware fine-tuning compare to other fine-tuning methods, such as prompt-based fine-tuning or adversarial fine-tuning, in terms of performance and adaptability?
- What are the potential limitations and challenges of using context-aware fine-tuning for domain adaptation, such as increased computational complexity or overfitting?
- Can you provide examples of successful applications of context-aware fine-tuning for domain adaptation in natural language processing or computer vision tasks?
- How can context-aware fine-tuning be combined with other techniques, such as transfer learning or multi-task learning, to further improve domain adaptation performance?
- What are the potential applications and use cases of context-aware fine-tuning for domain adaptation in real-world scenarios, such as text classification or image recognition?
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