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Related Questions
- What is the impact of out-of-vocabulary (OOV) words on the performance of large language models (LLMs)?
- How do subword modeling techniques, such as WordPiece and BPE, address OOV word handling in LLMs?
- What is the role of language modeling objectives, such as masked language modeling and next sentence prediction, in improving OOV word handling?
- How do techniques like character-level modeling and graph-based modeling handle OOV words in LLMs?
- What is the effect of pre-training and fine-tuning on OOV word handling in LLMs?
- How do techniques like knowledge graph-based modeling and external knowledge retrieval improve OOV word handling?
- What are some challenges and limitations of OOV word handling in LLMs, and how can they be addressed?
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