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Related Questions
- What are the key factors to consider when selecting a pre-trained language model for prompt engineering?
- How can the level of contextualization of a pre-trained model affect its interpretability and performance in a specific task?
- What are some strategies for optimizing the trade-off between interpretability and context-dependent performance in prompt engineering with pre-trained models?
- Can you explain how the size and complexity of a pre-trained model impact its ability to generalize to new, unseen contexts?
- How does the use of fine-tuning vs. few-shot learning affect the interpretability and context-dependent performance of a pre-trained model?
- What are some common pitfalls to avoid when using pre-trained language models in prompt engineering, and how can they be mitigated?
- Can you discuss the role of hyperparameter tuning in achieving a balance between interpretability and context-dependent performance in prompt engineering with pre-trained models?
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