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Related Questions
- What are some common data augmentation techniques used to mitigate bias in language models?
- How can data augmentation be used to increase the diversity of language models and reduce representation bias?
- What are the potential risks of relying solely on data augmentation to address representation bias in language models?
- Can you provide examples of successful applications of data augmentation to reduce bias in language models?
- How can data augmentation be used to improve the fairness of language models in specific domains, such as healthcare or finance?
- What are some challenges in implementing data augmentation to reduce bias in language models, and how can they be addressed?
- How does data augmentation compare to other methods for reducing bias in language models, such as debiasing or fairness-aware training?
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