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Related Questions
- What are the benefits of using real-world examples in training data for a language model like Qwen?
- How can diverse training data help Qwen generalize better to novel situations?
- What are some potential challenges in collecting and incorporating diverse, real-world examples into Qwen's training data?
- Can you explain how augmenting Qwen's training data with real-world examples can improve its ability to handle out-of-distribution inputs?
- How does the quality and relevance of real-world examples impact Qwen's performance in novel situations?
- What are some strategies for ensuring that Qwen's training data remains diverse and representative of real-world scenarios?
- Can you discuss the potential trade-offs between increasing the size and diversity of Qwen's training data versus improving its model architecture or fine-tuning its parameters?
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