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Related Questions
- How do LLMs handle polysemous words with multiple senses, such as 'bank' which can refer to a financial institution or the side of a river?
- What are some common strategies used by LLMs to disambiguate homographs like 'bat' which can be a flying mammal or a sports equipment?
- Can you explain the role of contextual information in helping LLMs resolve word sense ambiguity, such as 'light' which can be a source of illumination or not heavy?
- How do LLMs use semantic role labeling to disambiguate words with multiple meanings, such as 'run' which can be a verb or a noun?
- What is the impact of word sense induction on the performance of LLMs in tasks like question answering and natural language processing?
- Can you discuss the trade-offs between using explicit word sense disambiguation techniques versus relying on implicit context and inference in LLMs?
- How do LLMs handle cases where a word has multiple meanings, but the context is ambiguous, such as 'cloud' which can refer to a weather phenomenon or a computing term?
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