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Related Questions
- What are the primary goals of data augmentation in language models, and how does it differ from traditional data preprocessing?
- How does noise injection relate to data augmentation in the context of language models, and what are its benefits?
- Can you provide examples of data augmentation techniques commonly used in language models, such as word substitution, insertion, and deletion?
- How does data augmentation impact the training and testing of language models, particularly in terms of overfitting and generalization?
- What are the trade-offs between data augmentation and noise injection, and how do they affect the performance of language models?
- Can you explain the concept of adversarial training in language models and its connection to data augmentation and noise injection?
- How can data augmentation and noise injection be used to improve the robustness of language models to out-of-vocabulary words and rare events?
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