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Related Questions
- What is the core principle of active learning that enables it to reduce the need for human evaluation?
- In what ways does active learning prioritize selecting the most informative samples for human evaluation, thereby reducing the overall burden?
- How does active learning's iterative feedback loop between the model and human evaluator contribute to its ability to minimize human evaluation?
- What metrics or algorithms are commonly used in active learning to quantify the informativeness of each sample, guiding the human evaluator's attention?
- Can you explain how active learning's focus on optimizing model performance rather than simply accumulating more data affects the need for human evaluation?
- In situations where the dataset is small or imbalanced, how does active learning adapt its approach to ensure that the most critical samples are presented to human evaluators for annotation?
- Are there specific applications or domains where active learning has shown the greatest potential for reducing the need for human evaluation, such as in clinical diagnosis or product recommendations?
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