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Related Questions
- What are the primary challenges associated with scaling up pre-training for large language models, and how do they impact generalizability?
- How do the computational requirements of pre-training increase exponentially with the size of the model, and what strategies can be employed to mitigate this?
- What is the relationship between model capacity and the complexity of the tasks that can be learned, and how does this impact the generalizability of pre-trained models?
- Can you explain the concept of overfitting in the context of pre-training, and how it affects the ability of a model to generalize to new tasks?
- How do different optimization algorithms and hyperparameters affect the efficiency and effectiveness of pre-training, and what are some best practices for selecting them?
- What role does batch size play in the pre-training process, and how does it impact the balance between model training speed and generalizability?
- Can you discuss the trade-offs between model size, computational resources, and training time when scaling up pre-training, and how can these be optimized for maximum generalizability?
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