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Related Questions
- What are some common challenges data curators face when ensuring the representativeness of their datasets, and how can active learning address these challenges?
- Can you elaborate on the role of human annotators in the active learning process, and how they contribute to improving dataset representativeness?
- How do data curators balance the trade-off between increasing dataset size and the need for more accurate human annotations in the active learning approach?
- In what ways can data curators use active learning to reduce the annotation burden and still achieve representative datasets?
- What are some effective strategies for selecting which samples to present to human annotators in the active learning process, and why?
- Can you provide examples of successful human-in-the-loop approaches that have improved dataset representativeness in real-world scenarios?
- How do data curators measure and evaluate the representativeness of their datasets after applying active learning and human-in-the-loop approaches?
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