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Related Questions
- What are some generalization techniques used to improve the ability of a pre-trained model to adapt to new tasks?
- How does the size of a pre-trained model's embedding layer affect its ability to generalize to unseen domains?
- What is the relationship between model capacity and generalization performance in large language models?
- Can you explain the concept of 'in-domain' and 'out-of-domain' generalization in the context of pre-trained models?
- How does the choice of pre-training objective affect the ability of a model to generalize to new tasks?
- What are some common pitfalls to avoid when fine-tuning a pre-trained model for a new task?
- Can you discuss the trade-offs between model size, computational resources, and generalization performance?
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