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Related Questions
- What are the common strategies used by large language models to adapt to novel words or tokens?
- How does the concept of out-of-vocabulary (OOV) words affect the performance of pre-trained models in real-world applications?
- Can you explain the role of subword tokenization in enabling pre-trained models to handle novel words or tokens?
- What techniques are used to fine-tune pre-trained models for specific tasks or domains, allowing them to adapt to novel words or tokens?
- How does the ability of pre-trained models to generate text based on context help them adapt to novel words or tokens?
- What is the impact of the size of the training dataset on the ability of pre-trained models to adapt to novel words or tokens?
- Can you discuss the trade-offs between the ability of pre-trained models to adapt to novel words or tokens and their ability to generalize to new tasks?
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