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Related Questions
- What are some common challenges that large language models face when handling out-of-distribution data?
- How do large language models typically handle cases where the input data is outside of their known distribution?
- What techniques can be used to improve the generalization of large language models to out-of-distribution data?
- How do large language models represent uncertainty in their predictions, and what are the implications for decision-making?
- Can you explain the concept of calibration in the context of large language models, and how it relates to out-of-distribution generalization?
- What are some popular methods for measuring the uncertainty of large language models, and how can they be used to improve performance?
- How do large language models handle situations where the input data is ambiguous or open-ended, and what strategies can be used to improve performance in such cases?
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