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Related Questions
- Can you elaborate on how transfer learning enables pre-trained models to adapt to new, unseen domains?
- How do abstract concept representations in pre-trained models facilitate generalization across distinct domains?
- Can you describe the cognitive process that allows pre-trained models to recognize relationships between abstract concepts and apply this understanding to novel domains?
- How do the hierarchical representations of information in pre-trained models allow for more effective generalization across domains?
- Can you explain how pre-trained models' abilities in natural language processing support generalization across domains when processing text?
- What role do conceptual metaphors play in a pre-trained model's generalization across domains, where analogies between abstract and concrete concepts are drawn from a broad range of cognitive resources?
- How do pre-trained models handle semantic drift, leveraging the connections between abstract concept representations to adapt to slightly modified contexts?
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