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Related Questions
- What are the potential biases in training data that can impact a language model's ability to generalize to diverse populations?
- How does the representation of underrepresented groups in training data influence a language model's performance on out-of-distribution tasks?
- What are the implications of using training data that is predominantly sourced from a single geographic region or demographic group on a language model's generalizability?
- Can you explain how the choice of training data can affect a language model's ability to understand and respond to nuances in language that are specific to diverse populations?
- What are some strategies for collecting and incorporating diverse training data to improve a language model's generalizability?
- How does the quality and quantity of training data impact a language model's ability to adapt to new languages, dialects, or cultural contexts?
- What are the trade-offs between using a large, diverse training dataset versus a smaller, more focused dataset that is specifically tailored to a particular population or domain?
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