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Related Questions
- How do LLMs handle words that are not in their training data when generating prompts?
- Can you explain the concept of out-of-vocabulary (OOV) words and how they are handled in LLM prompts?
- How does word embedding help with handling OOV words in LLM prompts?
- What are some common methods for handling OOV words in LLM prompts, and how do they impact performance?
- Can you give an example of how to use a word embedding layer to handle OOV words in an LLM?
- How do the benefits of using word embeddings compare to other methods for handling OOV words in LLM prompts?
- What are some trade-offs to consider when choosing between handling OOV words through word embeddings or other methods in an LLM?
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