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Related Questions
- How does semantic space facilitate the creation of novel and unexpected connections between words in self-supervised learning?
- Can you explain the relationship between semantic space and the generation of surprising metaphors in the context of language models?
- What are the key characteristics of semantic space that enable self-supervised learning models to produce creative and innovative metaphors?
- How does the dimensionality of semantic space impact the ability of self-supervised learning models to generate surprising metaphors?
- What role does the concept of 'metaphorical distance' play in the generation of surprising metaphors in semantic space?
- Can you provide examples of how self-supervised learning models use semantic space to create surprising metaphors that are both creative and coherent?
- How does the use of semantic space in self-supervised learning models compare to traditional approaches to metaphor generation, such as rule-based systems?
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