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Related Questions
- What are the key differences between fine-tuning and prompt engineering, and how do they impact model performance?
- Can you provide examples of how prompt engineering can improve the accuracy of a model on a specific downstream task?
- How does the choice of prompt design influence the model's ability to generalize to new, unseen data?
- What are some common pitfalls to avoid when designing prompts for fine-tuning, and how can they be mitigated?
- In what ways can prompt engineering be used to adapt a pre-trained model to a new domain or task?
- How does the use of natural language prompts compare to other methods of fine-tuning, such as data augmentation or multitask learning?
- What are some best practices for evaluating the effectiveness of prompt engineering on downstream tasks?
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