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Related Questions
- What are some common pitfalls to avoid when designing prompts for few-shot learning to prevent overfitting?
- How can the use of control variables in prompts impact the generalizability of few-shot learning models?
- What are some strategies for creating more diverse and representative prompt sets to reduce overfitting in few-shot learning?
- Can you explain the concept of 'prompt engineering for generalization' and how it relates to reducing overfitting?
- What is the role of prompt length and complexity in contributing to overfitting in few-shot learning, and how can it be mitigated?
- How can the use of natural language processing (NLP) techniques, such as tokenization and stopword removal, impact prompt engineering for few-shot learning?
- What are some best practices for iteratively refining and testing prompts to ensure they are effective and generalizable in few-shot learning?
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