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Related Questions
- What are some common pitfalls to avoid when using pre-trained language models as a starting point for prompt engineering?
- How can the choice of pre-training data and model architecture impact the effectiveness of transfer learning in prompt engineering?
- What are some strategies for adapting pre-trained models to specific domains or tasks, and what are the potential trade-offs?
- How can the use of transfer learning in prompt engineering affect the interpretability and explainability of large language models?
- What are some best practices for fine-tuning pre-trained models for specific tasks, and how can this impact the overall performance of the model?
- Can you discuss the role of domain adaptation in transfer learning for prompt engineering, and how it can be used to improve model performance?
- What are some challenges associated with using transfer learning in multi-task learning scenarios, and how can they be addressed?
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