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Related Questions
- How does data augmentation impact the performance of large language models in real-world applications?
- Can you explain the concept of domain adaptation in machine learning and its relevance to language models?
- What are some common techniques used for data augmentation in natural language processing tasks?
- How does data augmentation help to reduce overfitting in large language models?
- What are the differences between data augmentation and transfer learning in language models?
- Can you provide examples of successful applications of data augmentation in large language models?
- What are some challenges associated with data augmentation in language models and how to address them?
- How does data augmentation impact the interpretability of large language models?
- Can you elaborate on the role of data augmentation in handling out-of-distribution data in language models?
- What are the potential risks and limitations of relying solely on data augmentation for improving generalizability?
- Can you discuss the relationship between data augmentation and active learning in language models?
- How does data augmentation affect the computational resources required for training large language models?
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