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Related Questions
- What are some common pitfalls to avoid when using data augmentation for language model robustness?
- How can data augmentation techniques be adapted to handle out-of-vocabulary words effectively?
- What are the potential limitations of using data augmentation to improve language model robustness in low-resource languages?
- Can you explain the concept of 'data hallucination' in the context of data augmentation and its implications for language model robustness?
- How does the quality of the data augmentation process impact the overall robustness of the language model?
- What are some strategies for selecting the most effective data augmentation techniques for a given language model and task?
- How can data augmentation be used in conjunction with other techniques, such as transfer learning and knowledge distillation, to improve language model robustness?
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