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Related Questions
- How do word embeddings like Word2Vec and GloVe represent word meanings in vector space?
- Can you explain the concept of semantic similarity in word embeddings and how it relates to word meanings?
- How do language models use context to disambiguate word meanings and improve the accuracy of word embeddings?
- What are some common challenges in training word embeddings and how do they impact the capture of nuanced word meanings?
- How do language models use word embeddings to capture subtle differences in word connotations and associations?
- Can you discuss the role of pre-training and fine-tuning in language models and how it affects the quality of word embeddings?
- How do language models use word embeddings to capture idiomatic expressions and figurative language, and what are some limitations of current approaches?
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