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Related Questions
- How can we balance the trade-off between tokenization and data augmentation to improve model performance?
- What techniques can be used to reduce the impact of tokenization on the quality of augmented data?
- Can you explain the concept of over-tokenization and how it affects data augmentation?
- What is the relationship between tokenization and data augmentation in the context of large language models?
- How can we use subword tokenization to improve the quality of augmented data?
- What are some strategies to minimize the loss of contextual information during tokenization?
- Can you discuss the role of tokenization in data augmentation for low-resource languages?
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