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Related Questions
- What are the key principles of prompt engineering and how can they be applied to adapt multi-task learning models to new tasks?
- How can data augmentation techniques be used in conjunction with prompt engineering to adapt multi-task learning models to new domains?
- What are some strategies for designing effective prompts that can help multi-task learning models generalize to new tasks or domains?
- Can you provide examples of successful applications of prompt engineering in adapting multi-task learning models to new tasks or domains?
- How does the concept of 'few-shot learning' relate to prompt engineering in the context of adapting multi-task learning models to new tasks?
- What are the challenges and limitations of using prompt engineering to adapt multi-task learning models to new tasks or domains?
- How can the performance of multi-task learning models be evaluated and optimized when using prompt engineering to adapt to new tasks or domains?
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