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Related Questions
- What are some common pitfalls in designing prompts for multi-task learning models that can lead to overfitting or underfitting?
- How can poorly designed prompts cause a model to focus too much on a specific task and neglect others, leading to overfitting?
- Can you explain how the lack of diverse and representative training data can result in underfitting due to poorly designed prompts?
- How does the ambiguity in prompts affect the model's ability to learn and generalize to new tasks?
- What are some strategies for designing effective prompts that minimize the risk of overfitting or underfitting in multi-task learning models?
- Can you discuss the impact of prompt engineering on the model's ability to handle out-of-distribution tasks and how to mitigate it?
- How can the evaluation metrics and prompt design be aligned to ensure that the model is learning the intended tasks and not overfitting or underfitting?
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