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Related Questions
- What are the key differences between active learning and other machine learning methods, and how can they be leveraged to refine prompts?
- Can you explain the concept of uncertainty sampling and how it can be applied to select the most informative prompts for active learning?
- How can I use human evaluation and feedback to refine my prompts and improve the model's performance in active learning?
- What are some strategies for selecting the right metric to evaluate the performance of the model in active learning, and how can it be used to refine prompts?
- Can you provide examples of how to use active learning to adapt to changing data distributions or concept drift, and how it can improve the model's performance?
- What are the challenges and limitations of using active learning in prompt engineering, and how can they be addressed?
- How can I use active learning to improve the robustness and generalizability of the model's performance, and what are some best practices for doing so?
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