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Related Questions
- What are the key strategies used in prompt engineering to prevent overfitting in language models?
- How does careful selection of input data and prompts help mitigate the risk of prompt overfitting?
- What role does the use of diverse and representative training data play in reducing the likelihood of prompt overfitting?
- Can you explain the concept of 'prompt design' and how it relates to reducing the risk of overfitting?
- How does the use of explicit and implicit feedback mechanisms help mitigate the risk of prompt overfitting?
- What is the relationship between prompt engineering and model generalization in the context of language models?
- Can you discuss the importance of testing and validation in prompt engineering to ensure that models are not overfitting to specific prompts?
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