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Related Questions
- What are some common challenges that LLMs face when processing idiomatic language and figurative speech?
- How do LLMs currently handle nuanced language such as sarcasm and humor, and what are the implications for their performance?
- Can LLMs learn to recognize and understand idioms, colloquialisms, and other forms of figurative language through supervised or unsupervised learning?
- How do the characteristics of LLMs, such as their training data and architecture, impact their ability to process figurative language?
- What are some potential applications of LLMs in understanding and generating figurative language, and how might they be used in real-world scenarios?
- What are some key limitations and biases in current LLMs that affect their ability to understand and generate idiomatic language?
- Can LLMs be fine-tuned to improve their performance on specific types of figurative language, such as sarcasm or irony, and if so, how?
- How do LLMs compare to other NLP models, such as rule-based systems or neural networks, in terms of their ability to process figurative language?
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