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Related Questions
- What are the key characteristics of a large pre-training dataset that contribute to a language model's generalization ability?
- Can you explain the relationship between dataset size and the model's ability to adapt to new tasks and domains?
- How does the diversity of the pre-training dataset impact the model's capacity to generalize to unseen tasks?
- What are some potential consequences of using a small or biased pre-training dataset on a language model's performance?
- Can you discuss the trade-offs between increasing the size of the pre-training dataset and the computational resources required to train the model?
- How does the quality of the pre-training dataset compare to its quantity in terms of impact on the model's generalization ability?
- What are some strategies for curating a diverse and representative pre-training dataset that can improve a language model's generalization to unseen tasks?
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