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Related Questions
- How does large-scale pre-training affect the ability of a language model to generalize to new tasks?
- Can you explain the relationship between pre-training size and the quality of generated text?
- What are some common techniques used to fine-tune large pre-trained language models for specific NLP tasks?
- How does the choice of pre-training objective (e.g. masked language modeling, next sentence prediction) impact the model's performance?
- What are some potential risks or limitations associated with using large-scale pre-trained language models?
- Can you discuss the trade-offs between the size of the pre-trained model and its computational requirements?
- How does the quality of pre-training data impact the overall performance of a large pre-trained language model?
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