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Related Questions
- What are the primary methods to implement data augmentation for defending against adversarial attacks in LLMs?
- Can data augmentation be used in conjunction with other defense techniques, such as adversarial training?
- How can data augmentation be applied to different types of LLMs, such as sequential and non-sequential models?
- Are there any specific data augmentation techniques that are more effective against certain types of adversarial attacks?
- Can data augmentation help improve the robustness of LLMs to various types of noise and variations in input data?
- What are some potential challenges and limitations of using data augmentation to mitigate adversarial attacks in LLMs?
- Can data augmentation be used to improve the robustness of LLMs to out-of-distribution inputs and edge cases?
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