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Related Questions
- What are the potential biases that can arise from training data sets with limited geographical representation?
- How do the sources of training data affect the generalizability of large language models across different cultural contexts?
- Can you explain the concept of 'data drift' in machine learning and its impact on model performance?
- What role does data curation play in maintaining the balance between diversity and quality in large training datasets?
- How do different data curation methods, such as active learning and transfer learning, impact the diversity of training data?
- Can you discuss the trade-offs between data quantity and diversity in training large language models?
- What are some strategies for ensuring that training data sets are representative of diverse user experiences and perspectives?
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