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Related Questions
- How does data augmentation impact the generalizability of large language models in out-of-distribution environments?
- What are the key factors that influence the trade-off between data augmentation and overfitting in LLMs?
- Can you explain the concept of overfitting in the context of large language models and how it relates to out-of-distribution data?
- How can data augmentation strategies be used to mitigate the risk of overfitting in LLMs when dealing with out-of-distribution data?
- What are some common data augmentation techniques used in large language models and how do they address the issue of overfitting?
- How does the choice of data augmentation technique impact the performance of LLMs on out-of-distribution data?
- What are some evaluation metrics that can be used to assess the impact of data augmentation on the trade-off between overfitting and generalization in LLMs?
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