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Related Questions
- What is the relationship between the size of the training dataset and the quality of the pre-trained model?
- How does the complexity of the tasks used for pre-training affect the generalizability of the model?
- Can you explain the concept of 'scaling up' in the context of pre-training and its impact on model performance?
- How does the number of training samples influence the capacity of the model to learn and generalize?
- What are some common challenges that arise when trying to scale up pre-training for complex tasks?
- Can you discuss the trade-offs between increasing the size of the training dataset and the computational resources required for pre-training?
- How do the architecture and design of the model influence its ability to generalize to new tasks and domains?
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