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Related Questions
- What are the primary methods of data augmentation for LLMs, and how do they impact model performance?
- Can you explain the concept of in-distribution and out-of-distribution data in the context of LLMs, and why data augmentation is crucial for handling OOD data?
- How does data augmentation affect the interpretability of LLMs, and what are the potential consequences of over-augmenting or under-augmenting the training data?
- What are some common challenges associated with applying data augmentation to LLMs, particularly when dealing with out-of-distribution data?
- Can you discuss the role of data augmentation in LLMs for handling concept drift, and how it can be used to improve model adaptability?
- How can data augmentation be used to improve the robustness of LLMs to adversarial attacks, and what are the implications for model interpretability?
- What are some best practices for implementing data augmentation in LLMs, particularly when working with out-of-distribution data, to maintain model interpretability?
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