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Related Questions
- What are the key differences in designing prompts for multi-task learning models versus single-task models?
- How does prompt engineering influence the ability of models to generalize to unseen tasks or tasks with varying input distributions?
- Can you explain the concept of 'prompt sniffing' and its relation to multi-task learning models?
- What are some best practices for crafting prompts that facilitate effective multi-task learning?
- How does the quality of training data affect the adaptability of multi-task learning models to changing task distributions?
- Can you discuss the role of prompt engineering in reducing overfitting in multi-task learning models?
- What are some common pitfalls to avoid when designing prompts for multi-task learning models, and how can they be mitigated?
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