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Related Questions
- How does data augmentation impact the diversity of training data for LLMs in explanation generation?
- Can you explain the effect of data augmentation on the robustness of LLMs in generating accurate explanations?
- In what ways can data augmentation be used to improve the interpretability of LLMs in explanation generation?
- How does data augmentation influence the transferability of LLMs trained for explanation generation to new domains?
- What are the trade-offs between data augmentation and other methods for improving LLMs in explanation generation?
- Can you discuss the role of data augmentation in addressing the limitations of LLMs in generating explanations for complex or nuanced concepts?
- How does data augmentation interact with other techniques, such as prompt engineering, in improving LLMs for explanation generation?
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