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Related Questions
- What are the key differences between active learning and traditional supervised learning in the context of prompt engineering?
- How do active learning strategies such as uncertainty sampling and query-by-committee address the issue of labeling noise in prompt engineering?
- Can you explain how active learning can help mitigate the variability in human annotator judgments in prompt engineering?
- What are some common active learning algorithms used in prompt engineering to handle noisy and variable labels?
- How does active learning impact the overall efficiency of the prompt engineering process in terms of label collection and model training?
- In what ways can active learning be used to adapt to changing user preferences and goals in prompt engineering?
- What are the potential limitations and challenges of applying active learning to prompt engineering, particularly in high-noise or high-variability scenarios?
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