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Related Questions
- How does data augmentation impact the generalizability of language models to real-world data?
- What are the different techniques used for oversampling in language models to reduce bias?
- Can you explain the concept of 'synthetic data' and its role in reducing bias in language models?
- How does oversampling affect the performance of language models on out-of-distribution data?
- What are some common pitfalls to avoid when using data augmentation and oversampling to reduce bias in language models?
- Can you discuss the relationship between data augmentation and the quality of the training data?
- How can data augmentation and oversampling be used in conjunction with other bias-reducing techniques, such as debiasing word embeddings?
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