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Related Questions
- What are the benefits of using ensemble methods for query selection in active learning for NLP tasks?
- How can we use data augmentation techniques to increase the diversity of queries in active learning for NLP?
- What are some strategies for incorporating out-of-distribution data in active learning for NLP to improve generalization?
- Can you explain the concept of 'representative sampling' in active learning for NLP and how it can be used to improve diversity in query selection?
- How can we use transfer learning to adapt a pre-trained model to a new task and improve diversity in query selection?
- What are the trade-offs between exploration-exploitation trade-offs in active learning for NLP and how can we balance them to improve diversity in query selection?
- Can you discuss the role of uncertainty-based sampling in active learning for NLP and how it can be used to select diverse queries?
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