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Related Questions
- Can large language models learn new words and vocabulary through exposure to text data?
- How do large language models handle cases where rare events or specific domain terminology are not covered in the training data?
- What techniques are used in large language models to avoid overfitting to rare events or domain-specific language?
- How does the use of subwords or word embeddings in large language models enable them to handle out-of-vocabulary words?
- In what ways are large language models more capable of learning from rare data points or outliers compared to traditional NLP models?
- Can large language models integrate external knowledge and domain expertise to improve handling of out-of-vocabulary words and rare events?
- How do the training methods and architectures used in large language models facilitate their handling of rare or unseen text data?
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