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Related Questions
- What is task-specific tokenization in the context of LLMs, and how does it affect vocabulary during fine-tuning?
- Can you explain how LLMs handle vocabulary overlap between tasks during fine-tuning, and what strategies are used to mitigate this issue?
- How do LLMs learn task-specific representations of words and subwords during fine-tuning, and what role does tokenization play in this process?
- What are some common techniques used for tokenization during LLM fine-tuning, and how do they impact vocabulary and model performance?
- Can you describe the process of vocabulary pruning during LLM fine-tuning, and how it affects model performance and memory usage?
- How do LLMs handle out-of-vocabulary (OOV) words during fine-tuning, and what strategies are used to mitigate the impact of OOV words on model performance?
- What is the relationship between tokenization, vocabulary, and model performance during LLM fine-tuning, and how can these factors be optimized for better results?
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