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Related Questions
- How do different pre-training objectives affect a large language model's ability to generalize to out-of-domain tasks in the context of few-shot learning?
- Can you discuss the impact of masked language modeling versus next sentence prediction on a model's performance in few-shot learning across different domains?
- In what ways do transformer-based architectures and other modern neural network designs contribute to a model's ability to generalize in few-shot learning?
- How can pre-training objectives that favor generalization, such as multi-task learning or prompt-based learning, be more effective in few-shot learning scenarios?
- Are there any pre-training techniques or objectives that have been shown to improve out-of-domain generalization in the context of few-shot learning?
- Can you explain the role of training data quality and quantity on a model's ability to generalize in few-shot learning across different domains?
- How do hyperparameter tuning and model architecture variations affect a model's out-of-domain generalization performance in few-shot learning tasks?
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