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Related Questions
- What are the primary factors that influence the trade-off between diversity and coherence in LLM-generated metaphors?
- How do self-supervised learning methods, such as masked language modeling, impact the balance between diversity and coherence in metaphor generation?
- Can you provide examples of how self-supervised learning can be used to optimize the trade-off between diversity and coherence in LLM-generated metaphors?
- What role do hyperparameters, such as learning rate and batch size, play in balancing diversity and coherence in LLM-generated metaphors?
- How do different pre-training objectives, such as language modeling and next sentence prediction, affect the trade-off between diversity and coherence in LLM-generated metaphors?
- Can you discuss the relationship between the level of self-supervision and the trade-off between diversity and coherence in LLM-generated metaphors?
- What are some potential challenges and limitations of using self-supervised learning to balance diversity and coherence in LLM-generated metaphors?
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