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Related Questions
- Can you explain the concept of multi-relational learning and how it is used to represent abstract concepts in knowledge graph embeddings?
- How do entity embeddings and relation embeddings interact to enable LLMs to capture nuanced aspects of emotions and intentions?
- What is the role of semantic spaces in enabling LLMs to model abstract concepts, and how do they facilitate the capture of context-dependent meanings?
- Can you elaborate on how knowledge graph embeddings can handle polysemy and ambiguity in natural language, particularly when it comes to abstract concepts like emotions and intentions?
- How do LLMs leverage the hierarchical structure of knowledge graphs to capture the nuances of abstract concepts and their relationships?
- What is the relationship between knowledge graph embeddings and ontologies, and how can they be used to enrich the understanding of abstract concepts in LLMs?
- Can you provide examples of how knowledge graph embeddings have been used to model complex abstract concepts, such as empathy or trust, in real-world applications?
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