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Related Questions
- What are the key differences between in-distribution and out-of-distribution data in the context of language models?
- How can we define a zero-shot scenario for evaluating the robustness of a language model to out-of-distribution data?
- What are some common methods used to assess the robustness of language models to out-of-distribution data in a zero-shot setting?
- Can you explain the concept of 'adversarial examples' in the context of language models and how they relate to out-of-distribution data?
- How can we use prompt engineering to create out-of-distribution data that is challenging for a language model to handle?
- What are some metrics or evaluation protocols that can be used to assess the robustness of a language model to out-of-distribution data in a zero-shot scenario?
- How can we use transfer learning or few-shot learning to improve the robustness of a language model to out-of-distribution data?
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