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Related Questions
- What are the challenges of polysemy in natural language processing, and how do large language models address them?
- Can you explain the concept of sense alignment in the context of large language models, and its significance in handling polysemy?
- How do large language models distinguish between different senses of a word with multiple meanings, and what techniques are used to achieve this?
- What is the impact of sense alignment on the overall performance of large language models, particularly in tasks such as question answering and text classification?
- How do large language models handle polysemy in out-of-vocabulary words, and what strategies are employed to mitigate the effects of polysemy in such cases?
- Can you discuss the relationship between sense alignment and other NLP tasks, such as entity recognition and sentiment analysis?
- What are some potential applications of sense alignment in large language models, and how can it be used to improve the accuracy and robustness of NLP systems?
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