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Related Questions
- What are some common challenges in adapting prompts across different domains and tasks, and how can transfer learning address these challenges?
- Can you explain the concept of knowledge distillation and how it can be applied to prompt engineering for transfer learning?
- How do prompt engineers typically select a pre-trained language model for transfer learning, and what factors influence this decision?
- What are some best practices for fine-tuning a pre-trained language model on a new domain or task, and how can prompt engineers evaluate the effectiveness of this fine-tuning?
- How can prompt engineers leverage multilingual language models for transfer learning, and what benefits do these models offer compared to monolingual models?
- Can you discuss the role of attention mechanisms in transfer learning, and how prompt engineers can use attention to adapt prompts across different domains and tasks?
- What are some common pitfalls to avoid when using transfer learning for prompt engineering, and how can prompt engineers mitigate these risks?
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