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Related Questions
- What techniques do large language models use to disambiguate polysemous words in different contexts?
- How do large language models handle homophones, homographs, and homonyms, and what are the implications for natural language understanding?
- Can you explain the role of word embeddings in helping large language models to handle polysemous words and their multiple meanings?
- How do large language models use contextual information to disambiguate polysemous words and resolve ambiguity in language understanding?
- What are the challenges and limitations of large language models in handling polysemous words, and how are researchers addressing these challenges?
- In what ways do large language models' handling of polysemous words impact their ability to perform tasks such as question answering, sentiment analysis, and text classification?
- Can you discuss the relationship between polysemous words and the concept of 'word sense induction' in large language models, and how it relates to word sense disambiguation?
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