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Related Questions
- What are the advantages of using contextualized embeddings to reduce bias in NLP models?
- Can you provide a clear explanation of how word2vec and GloVe compare to contextualized embeddings like BERT and XLNet?
- In what scenarios can contextualized embeddings lead to more informed decisions by reducing bias and ambiguity?
- Are contextualized embeddings more difficult to interpret than traditional bag-of-words models? How so?
- What challenges do contextualized embeddings face in terms of performance degradation and data quality impacts?
- Can contextualized embeddings be used effectively with out-of-vocabulary (OOV) tokens?
- How are contextualized embeddings used for common sense reasoning and nuance-based decision-making applications?
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