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Related Questions
- How do out-of-vocabulary (OOV) words affect the fine-tuning process for large language models like Llama and Qwen?
- Can you explain the impact of OOV words on the generalization and robustness of these models?
- What are some techniques to handle OOV words during fine-tuning, such as data augmentation, word embeddings, or neural machine translation?
- How do OOV words influence the model's ability to learn from noisy or out-of-domain data?
- What are some strategies to regularize the model and improve its ability to generalize to unseen words?
- Can you discuss the relationship between OOV words and the model's linguistic understanding, such as handling ambiguity or polysemy?
- How do OOV words impact the model's performance in downstream tasks, such as question answering or text classification?
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