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Related Questions
- Can you explain the concept of 'creative consistency' in the context of LLM-generated metaphors and how self-supervised learning can achieve it?
- How do you measure the coherence of metaphors generated by LLMs, and what techniques can be employed to improve coherence?
- What role does semantic clustering play in self-supervised learning for generating coherent and creative metaphors?
- Can you discuss the trade-offs between diversity and coherence in LLM-generated metaphors, and how self-supervised learning can balance these competing objectives?
- How do you incorporate user feedback into self-supervised learning to refine the generation of metaphors that are both creative and coherent?
- What are some common pitfalls to avoid when using self-supervised learning for generating metaphors, and how can they be addressed?
- Can you elaborate on the concept of 'metaphorical resonance' and how self-supervised learning can be used to generate metaphors that resonate with users on an emotional level?
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