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Related Questions
- How do large language models handle the gap between their training data and real-world common sense?
- What are some strategies for incorporating world knowledge and real-world experiences into text generation models?
- Can you explain the concept of 'adversarial testing' in the context of common sense reasoning in AI?
- How do models like BERT and RoBERTa handle implicit assumptions and inferences in text?
- What is the role of knowledge graphs in improving common sense reasoning in text generation models?
- Can you discuss the challenges of evaluating common sense reasoning in AI models and how they are addressed?
- How do researchers incorporate human feedback and evaluation metrics to improve common sense reasoning in AI models?
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