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Related Questions
- How does prompt engineering influence the quality of input data for few-shot learning tasks in large language models?
- What strategies can prompt engineers use to minimize overfitting on few-shot learning tasks?
- Can you provide examples of well-designed prompts that reduce overfitting in few-shot learning?
- How does the choice of prompt length impact the performance of large language models on few-shot learning tasks?
- What techniques can be used to optimize prompt engineering for reducing overfitting in few-shot learning?
- In what ways does prompt engineering affect the generalizability of large language models on few-shot learning tasks?
- What are some common pitfalls to avoid when designing prompts for few-shot learning tasks to prevent overfitting?
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