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Related Questions
- What is the optimal dataset size for augmenting a language model to improve its robustness to out-of-vocabulary words?
- How does the quality of the augmented dataset impact the language model's ability to generalize to unseen words?
- Can you explain the relationship between the size of the augmented dataset and the language model's performance on out-of-vocabulary words?
- What are some strategies for creating a high-quality augmented dataset that can improve the language model's robustness to out-of-vocabulary words?
- How does the language model's architecture and training objectives affect its ability to learn from an augmented dataset and improve its robustness to out-of-vocabulary words?
- What are some common pitfalls to avoid when creating an augmented dataset to improve the language model's robustness to out-of-vocabulary words?
- Can you discuss the trade-offs between the size and quality of the augmented dataset and the language model's computational resources and training time?
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