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Related Questions
- How can you leverage transfer learning to adapt pre-trained models to new out-of-distribution data in NLP?
- What are some techniques for augmenting in-distribution data with synthetic or generated data to improve model robustness?
- Can you explain the concept of data augmentation in the context of NLP and its benefits for out-of-distribution data?
- How do you determine the optimal amount of out-of-distribution data to include in the active learning process for NLP tasks?
- What are some strategies for selecting the most informative samples from out-of-distribution data for active learning in NLP?
- How can you measure the uncertainty of a model's predictions on out-of-distribution data and use it for active learning?
- Can you discuss the role of data preprocessing and feature engineering in incorporating out-of-distribution data in NLP active learning?
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