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Related Questions
- How do large language models like Llama handle out-of-vocabulary (OOV) words during fine-tuning?
- What strategies can be employed to improve model performance when encountering unknown or unseen words during fine-tuning?
- Can you explain the concept of subwording and how it relates to handling OOV words in large language models?
- How do models like Llama handle words that are similar in meaning but have different spellings or grammatical forms?
- What is the impact of OOV words on model performance, and how can it be mitigated?
- Can you discuss the trade-offs between model capacity, training data size, and the number of OOV words in large language models?
- What are some techniques for augmenting the training data to include more OOV words and improve model robustness?
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