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Related Questions
- How do word embeddings help to capture the nuances of word meanings in large language models?
- Can you explain the role of word embeddings in resolving polysemy and homograph issues in language models?
- In what ways do word embeddings contribute to improving the overall accuracy of large language models in understanding word senses?
- How do different types of word embeddings (e.g. Word2Vec, GloVe) affect the disambiguation of word senses in large language models?
- What are some common challenges in using word embeddings for disambiguating word senses in large language models?
- Can you discuss the relationship between word embeddings and context in disambiguating word senses in large language models?
- How do large language models leverage word embeddings to capture the subtleties of word meanings and senses in real-world applications?
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