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Related Questions
- How does data augmentation impact the robustness of LLMs in handling out-of-distribution data?
- Can you explain the relationship between data augmentation and the overfitting problem in LLMs?
- What are some common techniques used for data augmentation in LLMs, and how do they improve generalizability?
- How does data augmentation affect the interpretability of LLMs, and are there any trade-offs to consider?
- In what ways can data augmentation be used to improve the fairness of LLMs, particularly in scenarios with biased data?
- What are some challenges associated with data augmentation in LLMs, and how can they be addressed?
- Can you discuss the role of data augmentation in transfer learning for LLMs, and how it can enhance generalizability across domains?
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