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Related Questions
- How does data augmentation affect the accuracy of LLMs in healthcare applications when dealing with imbalanced datasets?
- Can data augmentation techniques help reduce bias in LLMs by introducing more diverse and representative training data?
- What are the potential risks of over-augmentation in LLMs for healthcare applications, and how can they be mitigated?
- How does data augmentation impact the interpretability of LLMs in healthcare applications, and are there any methods to improve it?
- Can data augmentation techniques help improve the generalizability of LLMs in healthcare applications across different patient populations?
- What are the most effective data augmentation techniques for reducing bias in LLMs for healthcare applications, and why?
- How can data augmentation be used in conjunction with other bias-reduction techniques, such as debiasing word embeddings, to improve LLM performance in healthcare applications?
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