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Related Questions
- How do LLMs handle rare or unseen words during training?
- What techniques do LLMs use to mitigate the impact of out-of-vocabulary words on model performance?
- Can you explain the concept of subwording in LLMs and its relation to handling out-of-vocabulary words?
- How do LLMs balance the trade-off between handling out-of-vocabulary words and maintaining model performance during inference?
- What role does the vocabulary size play in LLMs' ability to handle out-of-vocabulary words?
- Are there any specific techniques or architectures that are more effective at handling out-of-vocabulary words in LLMs?
- How do LLMs' performance metrics, such as perplexity or accuracy, relate to their ability to handle out-of-vocabulary words?
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