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Related Questions
- What are the key design choices in language model architectures that enable efficient learning from augmented datasets?
- How does the type of pre-training objective, such as masked language modeling or next sentence prediction, impact the model's ability to generalize to out-of-vocabulary words?
- What are some strategies for increasing the model's robustness to out-of-vocabulary words, such as through the use of subword modeling or character-level representation?
- How does the size and composition of the training dataset influence the model's ability to learn from augmented data and improve its robustness?
- Can you explain how the model's ability to learn from context and syntax affects its robustness to out-of-vocabulary words?
- What are some trade-offs between model complexity and robustness to out-of-vocabulary words, and how can these be addressed through careful tuning of model hyperparameters?
- How does the model's handling of rare and unseen words impact its ability to learn from augmented datasets and improve its robustness to out-of-vocabulary words?
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