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Related Questions
- What are some common types of OOD data used to evaluate large language models?
- How does the concept of OOD data differ from in-distribution data in the context of language models?
- What is the significance of evaluating a large language model's performance on OOD data?
- What are some common challenges associated with evaluating OOD performance in large language models?
- Can you explain the difference between OOD and out-of-class data in the context of language models?
- How do different types of OOD data (e.g. adversarial, noise, shifted) impact a large language model's performance?
- What are some techniques for adapting a large language model to perform well on OOD data, such as data augmentation and few-shot learning?
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