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Related Questions
- How can contextual information be used to detect out-of-distribution examples in LLMs and prevent misclassification?
- What are some common limitations of using contextual information to prevent adversarial examples in LLMs, and how can they be addressed?
- Can you explain the concept of 'contextual invariance' and its role in preventing out-of-distribution examples in LLMs?
- How does the use of contextual information impact the interpretability of LLMs, and what are the implications for model debugging and testing?
- What are some potential trade-offs between using contextual information to prevent out-of-distribution examples and maintaining model performance on in-distribution data?
- Can you discuss the relationship between contextual information and the concept of 'adversarial robustness' in LLMs, and how can contextual information be used to improve robustness?
- What are some potential challenges in incorporating contextual information into LLMs, such as data quality and availability, and how can these challenges be addressed?
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