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Related Questions
- Can large language models handle implicit knowledge or common sense that is not explicitly mentioned in their training data?
- How do LLMs generalize to new concepts or ideas that are not present in their training corpus?
- What techniques do LLMs use to infer or learn about concepts that are not explicitly mentioned in their training data?
- How do LLMs handle out-of-distribution or novel concepts that are not present in their training data?
- Can LLMs learn to reason about abstract concepts or ideas that are not explicitly mentioned in their training data?
- How do LLMs handle analogies or metaphors that are not explicitly mentioned in their training data?
- What is the role of prior knowledge or world knowledge in enabling LLMs to handle concepts that are not explicitly mentioned in their training data?
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