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Related Questions
- How do large language models handle homographs with multiple meanings, such as 'bank' referring to a financial institution or the side of a river?
- Can you explain how large language models distinguish between homonyms, such as 'flower' as a noun or 'flower' as a verb, and how they handle ambiguous words like 'bank'?
- In what ways do large language models account for polysemic words, which have multiple related meanings, such as 'head' referring to the top of the body or the leader of an organization?
- How do large language models learn to disambiguate words with multiple meanings, and what techniques do they use to handle context-dependent word meanings?
- Can you discuss the role of corpora, training data, and fine-tuning in helping large language models develop a nuanced understanding of homographs, homonyms, and polysemic words?
- What are some common challenges or limitations that large language models face when trying to learn and distinguish between homographs, homonyms, and polysemic words, and how are these limitations addressed?
- How do large language models handle words with multiple related senses, such as 'run' as a verb or 'run' as a noun, and how do they capture the subtle differences between these senses?
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