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Related Questions
- What are the key factors that influence the quality of training data for large language models?
- How does the diversity of training data impact a model's ability to generalize and be creative?
- Can biased data in the training set lead to creative output that perpetuates stereotypes or misinformation?
- What role does data curation play in ensuring that a model can balance context and creativity?
- How does the size of the training dataset affect the model's ability to understand nuanced context and generate creative content?
- What are some strategies for mitigating the negative impacts of low-quality training data on a model's creativity?
- Can a model that is trained on a diverse and high-quality dataset truly be said to be creative, or is it simply generating within established boundaries?
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