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Related Questions
- What strategies do large language models use to handle unknown or out-of-vocabulary words in text processing?
- How do large language models handle rare or domain-specific words that may not be present in their training data?
- What is the impact of out-of-vocabulary words on the accuracy and reliability of large language models in text ranking tasks?
- Can large language models learn to generate text using words that are not present in their training data?
- How do large language models handle words with multiple possible meanings or senses, such as homophones or homographs?
- What techniques are used by large language models to disambiguate words with multiple possible meanings in text ranking tasks?
- How do large language models adapt to new vocabulary and maintain their performance in text ranking tasks over time?
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