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Related Questions
- What are the common challenges that large language models face in detecting sarcasm and idioms?
- How do large language models use contextual information to determine the intended meaning of ambiguous language?
- Can you explain the differences between explicit and implicit sarcasm, and how models handle each?
- What role do emotional intelligence and empathy play in a large language model's ability to understand idioms and sarcasm?
- How do models handle idioms that have multiple possible interpretations or context-dependent meanings?
- What are some of the key features or components that enable large language models to detect sarcasm and idioms effectively?
- Can you provide examples of idioms and sarcasm that are difficult for large language models to detect, and why?
- How do large language models learn to recognize patterns and relationships in language that are relevant to detecting sarcasm and idioms?
- What are some of the potential biases or limitations that large language models may exhibit when it comes to understanding sarcasm and idioms?
- How do large language models use active learning or human-in-the-loop approaches to improve their ability to detect sarcasm and idioms?
- Can you discuss the importance of considering the cultural and linguistic nuances of idioms and sarcasm in large language model development?
- How do large language models handle idioms that have undergone semantic shift or evolution over time?
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