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Related Questions
- What are some common biases in natural language data that can affect few-shot learning in large language models?
- How can prompt engineering fail to generalize well to new, unseen data, and what are some strategies to mitigate this?
- What are some common pitfalls in crafting effective prompts for few-shot learning, such as ambiguity or leading questions?
- How can the overfitting of prompts to the training data occur and what techniques can be used to avoid it?
- What are some strategies for creating well-defined and unambiguous prompts for few-shot learning in large language models?
- How can the use of contextual information in prompts impact the performance of few-shot learning, and what are some best practices?
- What are some common issues with prompt templates and how can they be optimized for better performance in few-shot learning?
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