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Related Questions
- What are some common techniques for handling out-of-vocabulary words in LLMs?
- How can LLMs be augmented to improve their performance on words not seen during training?
- What are some strategies for mitigating the impact of out-of-vocabulary words on LLM accuracy?
- Can you explain the concept of subword modeling and its role in handling out-of-vocabulary words?
- How do techniques like character-level modeling and word-piece modeling address out-of-vocabulary words?
- What is the difference between a fixed vocabulary and a dynamic vocabulary in LLMs, and how do they impact out-of-vocabulary words?
- Can you discuss the trade-offs between using a larger training dataset, a more complex model architecture, and techniques like knowledge distillation to improve LLM performance on out-of-vocabulary words?
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