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Related Questions
- How do prompt engineers design effective few-shot learning prompts to adapt to new tasks and domains?
- What are the key differences between prompt engineering for few-shot learning and traditional machine learning model fine-tuning?
- Can you provide examples of how few-shot learning can be applied to adapt to new domains, and how prompt engineering plays a crucial role in this process?
- How do you handle out-of-distribution (OOD) data when adapting to new domains using few-shot learning and prompt engineering?
- What are some common pitfalls to avoid when using few-shot learning and prompt engineering for adapting to new tasks and domains?
- How can prompt engineering and few-shot learning be used together to improve the robustness of models to domain shifts?
- What role does meta-learning play in the intersection of prompt engineering and few-shot learning for adapting to new tasks and domains?
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