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Related Questions
- How does biased training data affect the accuracy and fairness of a large language model?
- What are the consequences of using a narrow and homogeneous training dataset on a language model's ability to generalize?
- Can you provide examples of real-world applications where diverse training data has led to improved model performance?
- What role does data curation play in ensuring that a language model is trained on representative and diverse data?
- How can data augmentation techniques be used to increase the diversity of a training dataset?
- What are some best practices for collecting and preprocessing data to ensure it is representative of different populations and contexts?
- In what ways can the lack of diversity in training data lead to biased language generation in a large language model?
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