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Related Questions
- How do contextualized embeddings improve the accuracy of responses in conversational AI models?
- What role do contextualized embeddings play in capturing nuanced relationships between words and their context?
- Can you provide examples of how contextualized embeddings are used in various NLP applications, such as question answering and sentiment analysis?
- How do contextualized embeddings account for the subtleties of language, such as idioms, collocations, and figurative language?
- How can contextualized embeddings be fine-tuned for specific domains or tasks to improve performance?
- What are the limitations of contextualized embeddings, and how can they be addressed through hybrid approaches or other techniques?
- Can you discuss the impact of contextualized embeddings on the interpretability of AI models, particularly in terms of feature importance and attribution?
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